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# --------------------------------------------------------------------
# BAZEL/Buildkite-CI test cases.
# --------------------------------------------------------------------
# To add new RLlib tests, first find the correct category of your new test
# within this file.
# All new tests - within their category - should be added alphabetically!
# Do not just add tests to the bottom of the file.
# Currently we have the following categories:
# - Learning tests/regression, tagged:
# -- "learning_tests_[discrete|continuous]": distinguish discrete
# actions vs continuous actions.
# -- "fake_gpus": Tests that run using 2 fake GPUs.
# - Quick agent compilation/tune-train tests, tagged "quick_train".
# NOTE: These should be obsoleted in favor of "algorithms_dir" tests as
# they cover the same functionaliy.
# - Folder-bound tests, tagged with the name of the top-level dir:
# - `env` directory tests.
# - `evaluation` directory tests.
# - `execution` directory tests.
# - `models` directory tests.
# - `offline` directory tests.
# - `policy` directory tests.
# - `utils` directory tests.
# - Trainer ("agents") tests, tagged "algorithms_dir".
# - Tests directory (everything in rllib/tests/...), tagged: "tests_dir" and
# "tests_dir_[A-Z]"
# - Examples directory (everything in rllib/examples/...), tagged: "examples" and
# "examples_[A-Z]"
# - Memory leak tests tagged "memory_leak_tests".
# Note: The "examples" and "tests_dir" tags have further sub-tags going by the
# starting letter of the test name (e.g. "examples_A", or "tests_dir_F") for
# split-up purposes in buildkite.
# Note: There is a special directory in examples: "documentation" which contains
# all code that is linked to from within the RLlib docs. This code is tested
# separately via the "documentation" tag.
# Additional tags are:
# - "team:rllib": Indicating that all tests in this file are the responsibility of
# the RLlib Team.
# - "needs_gpu": Indicating that a test needs to have a GPU in order to run.
# - "gpu": Indicating that a test may (but doesn't have to) be run in the GPU
# pipeline, defined in .buildkite/pipeline.gpu.yaml.
# - "multi-gpu": Indicating that a test will definitely be run in the Large GPU
# pipeline, defined in .buildkite/pipeline.gpu.large.yaml.
# - "no_gpu": Indicating that a test should not be run in the GPU pipeline due
# to certain incompatibilities.
# - "no_tf_eager_tracing": Exclude this test from tf-eager tracing tests.
# - "torch_only": Only run this test case with framework=torch.
# Our .buildkite/pipeline.yml and .buildkite/pipeline.gpu.yml files execute all
# these tests in n different jobs.
load("//bazel:python.bzl", "py_test_module_list")
# --------------------------------------------------------------------
# Agents learning regression tests.
#
# Tag: learning_tests
#
# This will test all yaml files (via `rllib train`)
# inside rllib/tuned_examples/[algo-name] for actual learning success.
# --------------------------------------------------------------------
# A2C
# py_test(
# name = "learning_tests_cartpole_a2c",
# main = "tests/run_regression_tests.py",
# tags = ["team:rllib", "", "learning_tests", "learning_tests_cartpole", "learning_tests_discrete"],
# size = "large",
# srcs = ["tests/run_regression_tests.py"],
# data = ["tuned_examples/a2c/cartpole-a2c.yaml"],
# args = ["--yaml-dir=tuned_examples/a2c"]
# )
py_test(
name = "learning_tests_cartpole_a2c_microbatch",
main = "tests/run_regression_tests.py",
tags = ["team:rllib", "exclusive", "learning_tests", "learning_tests_cartpole", "learning_tests_discrete"],
size = "large",
srcs = ["tests/run_regression_tests.py"],
data = ["tuned_examples/a2c/cartpole-a2c-microbatch.yaml"],
args = ["--yaml-dir=tuned_examples/a2c"]
)
py_test(
name = "learning_tests_cartpole_a2c_fake_gpus",
main = "tests/run_regression_tests.py",
tags = ["team:rllib", "exclusive", "learning_tests", "learning_tests_cartpole", "learning_tests_discrete", "fake_gpus"],
size = "large",
srcs = ["tests/run_regression_tests.py"],
data = ["tuned_examples/a2c/cartpole-a2c-fake-gpus.yaml"],
args = ["--yaml-dir=tuned_examples/a2c"]
)
# A3C
# py_test(
# name = "learning_tests_cartpole_a3c",
# main = "tests/run_regression_tests.py",
# tags = ["team:rllib", "exclusive", "learning_tests", "learning_tests_cartpole", "learning_tests_discrete"],
# size = "large",
# srcs = ["tests/run_regression_tests.py"],
# data = ["tuned_examples/a3c/cartpole-a3c.yaml"],
# args = ["--yaml-dir=tuned_examples/a3c"]
# )
# AlphaStar
py_test(
name = "learning_tests_cartpole_alpha_star",
main = "tests/run_regression_tests.py",
tags = ["team:rllib", "exclusive", "learning_tests", "learning_tests_cartpole", "learning_tests_discrete"],
size = "large",
srcs = ["tests/run_regression_tests.py"],
data = ["tuned_examples/alpha_star/multi-agent-cartpole-alpha-star.yaml"],
args = ["--yaml-dir=tuned_examples/alpha_star", "--num-cpus=10"]
)
# AlphaZero
py_test(
name = "learning_tests_cartpole_sparse_rewards_alpha_zero",
tags = ["team:rllib", "exclusive", "torch_only", "learning_tests", "learning_tests_discrete"],
main = "tests/run_regression_tests.py",
size = "large",
srcs = ["tests/run_regression_tests.py"],
data = ["tuned_examples/alpha_zero/cartpole-sparse-rewards-alpha-zero.yaml"],
args = ["--yaml-dir=tuned_examples/alpha_zero", "--num-cpus=8"]
)
# APEX-DQN
# py_test(
# name = "learning_tests_cartpole_apex",
# main = "tests/run_regression_tests.py",
# tags = ["team:rllib", "exclusive", "learning_tests", "learning_tests_cartpole", "learning_tests_discrete"],
# size = "large",
# srcs = ["tests/run_regression_tests.py"],
# data = [
# "tuned_examples/apex_dqn/cartpole-apex.yaml",
# ],
# args = ["--yaml-dir=tuned_examples/apex_dqn", "--num-cpus=6"]
# )
# Once APEX supports multi-GPU.
# py_test(
# name = "learning_cartpole_apex_fake_gpus",
# main = "tests/run_regression_tests.py",
# tags = ["team:rllib", "exclusive", "learning_tests", "learning_tests_cartpole", "learning_tests_discrete", "fake_gpus"],
# size = "large",
# srcs = ["tests/run_regression_tests.py"],
# data = ["tuned_examples/apex_dqn/cartpole-apex-fake-gpus.yaml"],
# args = ["--yaml-dir=tuned_examples/apex_dqn"]
# )
# APPO
py_test(
name = "learning_tests_cartpole_appo_no_vtrace",
main = "tests/run_regression_tests.py",
tags = ["team:rllib", "exclusive", "learning_tests", "learning_tests_cartpole", "learning_tests_discrete"],
size = "large",
srcs = ["tests/run_regression_tests.py"],
data = ["tuned_examples/appo/cartpole-appo.yaml"],
args = ["--yaml-dir=tuned_examples/appo"]
)
# py_test(
# name = "learning_tests_cartpole_appo_vtrace",
# main = "tests/run_regression_tests.py",
# tags = ["team:rllib", "exclusive", "learning_tests", "learning_tests_cartpole", "learning_tests_discrete"],
# size = "large",
# srcs = ["tests/run_regression_tests.py"],
# data = ["tuned_examples/appo/cartpole-appo-vtrace.yaml"],
# args = ["--yaml-dir=tuned_examples/appo"]
# )
py_test(
name = "learning_tests_cartpole_separate_losses_appo",
main = "tests/run_regression_tests.py",
tags = ["team:rllib", "tf_only", "exclusive", "learning_tests", "learning_tests_cartpole", "learning_tests_discrete"],
size = "large",
srcs = ["tests/run_regression_tests.py"],
data = [
"tuned_examples/appo/cartpole-appo-vtrace-separate-losses.yaml"
],
args = ["--yaml-dir=tuned_examples/appo"]
)
# py_test(
# name = "learning_tests_frozenlake_appo",
# main = "tests/run_regression_tests.py",
# tags = ["team:rllib", "exclusive", "learning_tests", "learning_tests_discrete"],
# size = "large",
# srcs = ["tests/run_regression_tests.py"],
# data = ["tuned_examples/appo/frozenlake-appo-vtrace.yaml"],
# args = ["--yaml-dir=tuned_examples/appo"]
# )
py_test(
name = "learning_tests_cartpole_appo_fake_gpus",
main = "tests/run_regression_tests.py",
tags = ["team:rllib", "exclusive", "learning_tests", "learning_tests_cartpole", "learning_tests_discrete", "fake_gpus"],
size = "large",
srcs = ["tests/run_regression_tests.py"],
data = ["tuned_examples/appo/cartpole-appo-vtrace-fake-gpus.yaml"],
args = ["--yaml-dir=tuned_examples/appo"]
)
# ARS
py_test(
name = "learning_tests_cartpole_ars",
main = "tests/run_regression_tests.py",
tags = ["team:rllib", "exclusive", "learning_tests", "learning_tests_cartpole", "learning_tests_discrete"],
size = "large",
srcs = ["tests/run_regression_tests.py"],
data = ["tuned_examples/ars/cartpole-ars.yaml"],
args = ["--yaml-dir=tuned_examples/ars"]
)
# CQL
py_test(
name = "learning_tests_pendulum_cql",
main = "tests/run_regression_tests.py",
tags = ["team:rllib", "exclusive", "learning_tests", "learning_tests_pendulum", "learning_tests_continuous"],
size = "large",
srcs = ["tests/run_regression_tests.py"],
# Include the zipped json data file as well.
data = [
"tuned_examples/cql/pendulum-cql.yaml",
"tests/data/pendulum/enormous.zip",
],
args = ["--yaml-dir=tuned_examples/cql"]
)
# CRR
py_test(
name = "learning_tests_pendulum_crr",
main = "tests/run_regression_tests.py",
tags = ["team:rllib", "torch_only", "learning_tests", "learning_tests_pendulum", "learning_tests_continuous"],
size = "large",
srcs = ["tests/run_regression_tests.py"],
# Include an offline json data file as well.
data = [
"tuned_examples/crr/pendulum-v1-crr.yaml",
"tests/data/pendulum/pendulum_replay_v1.1.0.zip",
],
args = ["--yaml-dir=tuned_examples/crr"]
)
py_test(
name = "learning_tests_cartpole_crr",
main = "tests/run_regression_tests.py",
tags = ["team:rllib", "torch_only", "learning_tests", "learning_tests_cartpole", "learning_tests_discrete"],
size = "large",
srcs = ["tests/run_regression_tests.py"],
# Include an offline json data file as well.
data = [
"tuned_examples/crr/cartpole-v0-crr.yaml",
"tests/data/cartpole/large.json",
],
args = ["--yaml-dir=tuned_examples/crr", '--framework=torch']
)
# DDPG
# py_test(
# name = "learning_tests_pendulum_ddpg",
# main = "tests/run_regression_tests.py",
# tags = ["team:rllib", "exclusive", "learning_tests", "learning_tests_pendulum", "learning_tests_continuous"],
# size = "large",
# srcs = ["tests/run_regression_tests.py"],
# data = glob(["tuned_examples/ddpg/pendulum-ddpg.yaml"]),
# args = ["--yaml-dir=tuned_examples/ddpg"]
# )
py_test(
name = "learning_tests_pendulum_ddpg_fake_gpus",
main = "tests/run_regression_tests.py",
tags = ["team:rllib", "exclusive", "learning_tests", "learning_tests_pendulum", "learning_tests_continuous", "fake_gpus"],
size = "large",
srcs = ["tests/run_regression_tests.py"],
data = ["tuned_examples/ddpg/pendulum-ddpg-fake-gpus.yaml"],
args = ["--yaml-dir=tuned_examples/ddpg"]
)
# DDPPO
py_test(
name = "learning_tests_cartpole_ddppo",
main = "tests/run_regression_tests.py",
tags = ["team:rllib", "exclusive", "torch_only", "learning_tests", "learning_tests_cartpole", "learning_tests_discrete"],
size = "large",
srcs = ["tests/run_regression_tests.py"],
data = glob(["tuned_examples/ddppo/cartpole-ddppo.yaml"]),
args = ["--yaml-dir=tuned_examples/ddppo"]
)
py_test(
name = "learning_tests_pendulum_ddppo",
main = "tests/run_regression_tests.py",
tags = ["team:rllib", "exclusive", "torch_only", "learning_tests", "learning_tests_pendulum", "learning_tests_continuous"],
size = "large",
srcs = ["tests/run_regression_tests.py"],
data = glob(["tuned_examples/ddppo/pendulum-ddppo.yaml"]),
args = ["--yaml-dir=tuned_examples/ddppo"]
)
# DQN
# py_test(
# name = "learning_tests_cartpole_dqn",
# main = "tests/run_regression_tests.py",
# tags = ["team:rllib", "exclusive", "learning_tests", "learning_tests_cartpole", "learning_tests_discrete"],
# size = "large",
# srcs = ["tests/run_regression_tests.py"],
# data = ["tuned_examples/dqn/cartpole-dqn.yaml"],
# args = ["--yaml-dir=tuned_examples/dqn"]
# )
py_test(
name = "learning_tests_cartpole_dqn_softq",
main = "tests/run_regression_tests.py",
tags = ["team:rllib", "exclusive", "learning_tests", "learning_tests_cartpole", "learning_tests_discrete"],
size = "large",
srcs = ["tests/run_regression_tests.py"],
data = ["tuned_examples/dqn/cartpole-dqn-softq.yaml"],
args = ["--yaml-dir=tuned_examples/dqn"]
)
# Does not work with tf-eager tracing due to Exploration's postprocessing
# method injecting a tensor into a new graph. Revisit when tf-eager tracing
# is better supported.
py_test(
name = "learning_tests_cartpole_dqn_param_noise",
main = "tests/run_regression_tests.py",
tags = ["team:rllib", "exclusive", "learning_tests", "learning_tests_cartpole", "learning_tests_discrete", "no_tf_eager_tracing"],
size = "large",
srcs = ["tests/run_regression_tests.py"],
data = ["tuned_examples/dqn/cartpole-dqn-param-noise.yaml"],
args = ["--yaml-dir=tuned_examples/dqn"]
)
py_test(
name = "learning_tests_cartpole_dqn_fake_gpus",
main = "tests/run_regression_tests.py",
tags = ["team:rllib", "exclusive", "learning_tests", "learning_tests_cartpole", "learning_tests_discrete", "fake_gpus"],
size = "large",
srcs = ["tests/run_regression_tests.py"],
data = ["tuned_examples/dqn/cartpole-dqn-fake-gpus.yaml"],
args = ["--yaml-dir=tuned_examples/dqn"]
)
# Simple-Q
py_test(
name = "learning_tests_cartpole_simpleq",
main = "tests/run_regression_tests.py",
tags = ["team:rllib", "exclusive", "learning_tests", "learning_tests_cartpole", "learning_tests_discrete"],
size = "large",
srcs = ["tests/run_regression_tests.py"],
data = [
"tuned_examples/simple_q/cartpole-simpleq.yaml",
],
args = ["--yaml-dir=tuned_examples/simple_q"]
)
py_test(
name = "learning_tests_cartpole_simpleq_fake_gpus",
main = "tests/run_regression_tests.py",
tags = ["team:rllib", "exclusive", "learning_tests", "learning_tests_cartpole", "learning_tests_discrete", "fake_gpus"],
size = "large",
srcs = ["tests/run_regression_tests.py"],
data = ["tuned_examples/simple_q/cartpole-simpleq-fake-gpus.yaml"],
args = ["--yaml-dir=tuned_examples/simple_q"]
)
# ES
# py_test(
# name = "learning_tests_cartpole_es",
# main = "tests/run_regression_tests.py",
# tags = ["team:rllib", "exclusive", "learning_tests", "learning_tests_cartpole", "learning_tests_discrete"],
# size = "large",
# srcs = ["tests/run_regression_tests.py"],
# data = ["tuned_examples/es/cartpole-es.yaml"],
# args = ["--yaml-dir=tuned_examples/es"]
# )
# IMPALA
# py_test(
# name = "learning_tests_cartpole_impala",
# main = "tests/run_regression_tests.py",
# tags = ["team:rllib", "exclusive", "learning_tests", "learning_tests_cartpole", "learning_tests_discrete"],
# size = "large",
# srcs = ["tests/run_regression_tests.py"],
# data = ["tuned_examples/impala/cartpole-impala.yaml"],
# args = ["--yaml-dir=tuned_examples/impala"]
# )
py_test(
name = "learning_tests_cartpole_impala_fake_gpus",
main = "tests/run_regression_tests.py",
tags = ["team:rllib", "exclusive", "learning_tests", "learning_tests_cartpole", "learning_tests_discrete", "fake_gpus"],
size = "large",
srcs = ["tests/run_regression_tests.py"],
data = ["tuned_examples/impala/cartpole-impala-fake-gpus.yaml"],
args = ["--yaml-dir=tuned_examples/impala"]
)
# MADDPG
py_test(
name = "learning_tests_two_step_game_maddpg",
main = "tests/run_regression_tests.py",
tags = ["team:rllib", "exclusive", "tf_only", "no_tf_eager_tracing", "learning_tests", "learning_tests_discrete"],
size = "large",
srcs = ["tests/run_regression_tests.py"],
data = ["tuned_examples/maddpg/two-step-game-maddpg.yaml"],
args = ["--yaml-dir=tuned_examples/maddpg", "--framework=tf"]
)
# Working, but takes a long time to learn (>15min).
# Removed due to Higher API conflicts with Pytorch-Import tests
## MB-MPO
#py_test(
# name = "learning_tests_pendulum_mbmpo",
# main = "tests/run_regression_tests.py",
# tags = ["team:rllib", "exclusive", "torch_only", "learning_tests", "learning_tests_pendulum", "learning_tests_continuous"],
# size = "large",
# srcs = ["tests/run_regression_tests.py"],
# data = ["tuned_examples/mbmpo/pendulum-mbmpo.yaml"],
# args = ["--yaml-dir=tuned_examples/mbmpo"]
#)
# PG
py_test(
name = "learning_tests_cartpole_pg",
main = "tests/run_regression_tests.py",
tags = ["team:rllib", "exclusive", "learning_tests", "learning_tests_cartpole", "learning_tests_discrete"],
size = "large",
srcs = ["tests/run_regression_tests.py"],
data = ["tuned_examples/pg/cartpole-pg.yaml"],
args = ["--yaml-dir=tuned_examples/pg"]
)
py_test(
name = "learning_tests_cartpole_crashing_pg",
main = "tests/run_regression_tests.py",
tags = ["team:rllib", "exclusive", "learning_tests", "learning_tests_cartpole", "learning_tests_discrete"],
size = "large",
srcs = ["tests/run_regression_tests.py"],
data = ["tuned_examples/pg/cartpole-crashing-pg.yaml"],
args = ["--yaml-dir=tuned_examples/pg"]
)
py_test(
name = "learning_tests_cartpole_crashing_with_remote_envs_pg",
main = "tests/run_regression_tests.py",
tags = ["team:rllib", "exclusive", "learning_tests", "learning_tests_cartpole", "learning_tests_discrete"],
size = "large",
srcs = ["tests/run_regression_tests.py"],
data = ["tuned_examples/pg/cartpole-crashing-with_remote-envs-pg.yaml"],
args = ["--yaml-dir=tuned_examples/pg"]
)
py_test(
name = "learning_tests_cartpole_pg_fake_gpus",
main = "tests/run_regression_tests.py",
tags = ["team:rllib", "exclusive", "learning_tests", "learning_tests_cartpole", "learning_tests_discrete", "fake_gpus"],
size = "large",
srcs = ["tests/run_regression_tests.py"],
data = ["tuned_examples/pg/cartpole-pg-fake-gpus.yaml"],
args = ["--yaml-dir=tuned_examples/pg"]
)
# PPO
# py_test(
# name = "learning_tests_cartpole_ppo",
# main = "tests/run_regression_tests.py",
# tags = ["team:rllib", "exclusive", "learning_tests", "learning_tests_cartpole", "learning_tests_discrete"],
# size = "large",
# srcs = ["tests/run_regression_tests.py"],
# data = ["tuned_examples/ppo/cartpole-ppo.yaml"],
# args = ["--yaml-dir=tuned_examples/ppo"]
# )
py_test(
name = "learning_tests_pendulum_ppo",
main = "tests/run_regression_tests.py",
tags = ["team:rllib", "exclusive", "learning_tests", "learning_tests_pendulum", "learning_tests_continuous"],
size = "large",
srcs = ["tests/run_regression_tests.py"],
data = ["tuned_examples/ppo/pendulum-ppo.yaml"],
args = ["--yaml-dir=tuned_examples/ppo"]
)
py_test(
name = "learning_tests_transformed_actions_pendulum_ppo",
main = "tests/run_regression_tests.py",
tags = ["team:rllib", "exclusive", "learning_tests", "learning_tests_pendulum", "learning_tests_continuous"],
size = "large",
srcs = ["tests/run_regression_tests.py"],
data = ["tuned_examples/ppo/pendulum-transformed-actions-ppo.yaml"],
args = ["--yaml-dir=tuned_examples/ppo"]
)
py_test(
name = "learning_tests_repeat_after_me_ppo",
main = "tests/run_regression_tests.py",
tags = ["team:rllib", "exclusive", "learning_tests", "learning_tests_discrete"],
size = "large",
srcs = ["tests/run_regression_tests.py"],
data = ["tuned_examples/ppo/repeatafterme-ppo-lstm.yaml"],
args = ["--yaml-dir=tuned_examples/ppo"]
)
py_test(
name = "learning_tests_cartpole_ppo_fake_gpus",
main = "tests/run_regression_tests.py",
tags = ["team:rllib", "exclusive", "learning_tests", "learning_tests_cartpole", "learning_tests_discrete", "fake_gpus"],
size = "large",
srcs = ["tests/run_regression_tests.py"],
data = ["tuned_examples/ppo/cartpole-ppo-fake-gpus.yaml"],
args = ["--yaml-dir=tuned_examples/ppo"]
)
# QMIX
py_test(
name = "learning_tests_two_step_game_qmix",
main = "tests/run_regression_tests.py",
tags = ["team:rllib", "exclusive", "learning_tests", "learning_tests_discrete"],
size = "large",
srcs = ["tests/run_regression_tests.py"],
data = ["tuned_examples/qmix/two-step-game-qmix.yaml"],
args = ["--yaml-dir=tuned_examples/qmix", "--framework=torch"]
)
py_test(
name = "learning_tests_two_step_game_qmix_vdn_mixer",
main = "tests/run_regression_tests.py",
tags = ["team:rllib", "exclusive", "learning_tests", "learning_tests_discrete"],
size = "large",
srcs = ["tests/run_regression_tests.py"],
data = ["tuned_examples/qmix/two-step-game-qmix-vdn-mixer.yaml"],
args = ["--yaml-dir=tuned_examples/qmix", "--framework=torch"]
)
py_test(
name = "learning_tests_two_step_game_qmix_no_mixer",
main = "tests/run_regression_tests.py",
tags = ["team:rllib", "exclusive", "learning_tests", "learning_tests_discrete"],
size = "large",
srcs = ["tests/run_regression_tests.py"],
data = ["tuned_examples/qmix/two-step-game-qmix-no-mixer.yaml"],
args = ["--yaml-dir=tuned_examples/qmix", "--framework=torch"]
)
# R2D2
py_test(
name = "learning_tests_stateless_cartpole_r2d2",
main = "tests/run_regression_tests.py",
tags = ["team:rllib", "exclusive", "learning_tests", "learning_tests_cartpole", "learning_tests_discrete"],
size = "large",
srcs = ["tests/run_regression_tests.py"],
data = ["tuned_examples/r2d2/stateless-cartpole-r2d2.yaml"],
args = ["--yaml-dir=tuned_examples/r2d2"]
)
py_test(
name = "learning_tests_stateless_cartpole_r2d2_fake_gpus",
main = "tests/run_regression_tests.py",
tags = ["team:rllib", "exclusive", "learning_tests", "learning_tests_cartpole", "fake_gpus"],
size = "large",
srcs = ["tests/run_regression_tests.py"],
data = ["tuned_examples/r2d2/stateless-cartpole-r2d2-fake-gpus.yaml"],
args = ["--yaml-dir=tuned_examples/r2d2"]
)
# SAC
py_test(
name = "learning_tests_cartpole_sac",
main = "tests/run_regression_tests.py",
tags = ["team:rllib", "exclusive", "learning_tests", "learning_tests_cartpole", "learning_tests_discrete"],
size = "large",
srcs = ["tests/run_regression_tests.py"],
data = ["tuned_examples/sac/cartpole-sac.yaml"],
args = ["--yaml-dir=tuned_examples/sac"]
)
# py_test(
# name = "learning_tests_cartpole_continuous_pybullet_sac",
# main = "tests/run_regression_tests.py",
# tags = ["team:rllib", "exclusive", "learning_tests", "learning_tests_cartpole", "learning_tests_continuous"],
# size = "large",
# srcs = ["tests/run_regression_tests.py"],
# data = ["tuned_examples/sac/cartpole-continuous-pybullet-sac.yaml"],
# args = ["--yaml-dir=tuned_examples/sac"]
# )
# py_test(
# name = "learning_tests_pendulum_sac",
# main = "tests/run_regression_tests.py",
# tags = ["team:rllib", "exclusive", "learning_tests", "learning_tests_pendulum", "learning_tests_continuous"],
# size = "large",
# srcs = ["tests/run_regression_tests.py"],
# data = ["tuned_examples/sac/pendulum-sac.yaml"],
# args = ["--yaml-dir=tuned_examples/sac"]
# )
# py_test(
# name = "learning_tests_transformed_actions_pendulum_sac",
# main = "tests/run_regression_tests.py",
# tags = ["team:rllib", "exclusive", "learning_tests", "learning_tests_pendulum", "learning_tests_continuous"],
# size = "large",
# srcs = ["tests/run_regression_tests.py"],
# data = ["tuned_examples/sac/pendulum-transformed-actions-sac.yaml"],
# args = ["--yaml-dir=tuned_examples/sac"]
# )
# py_test(
# name = "learning_tests_pendulum_sac_fake_gpus",
# main = "tests/run_regression_tests.py",
# tags = ["team:rllib", "exclusive", "learning_tests", "learning_tests_pendulum", "learning_tests_continuous", "fake_gpus"],
# size = "large",
# srcs = ["tests/run_regression_tests.py"],
# data = ["tuned_examples/sac/pendulum-sac-fake-gpus.yaml"],
# args = ["--yaml-dir=tuned_examples/sac"]
# )
# SlateQ
# py_test(
# name = "learning_tests_interest_evolution_10_candidates_recsim_env_slateq",
# main = "tests/run_regression_tests.py",
# tags = ["team:rllib", "exclusive", "learning_tests", "learning_tests_discrete"],
# size = "large",
# srcs = ["tests/run_regression_tests.py"],
# data = ["tuned_examples/slateq/interest-evolution-10-candidates-recsim-env-slateq.yaml"],
# args = ["--yaml-dir=tuned_examples/slateq"]
# )
py_test(
name = "learning_tests_interest_evolution_10_candidates_recsim_env_slateq_fake_gpus",
main = "tests/run_regression_tests.py",
tags = ["team:rllib", "exclusive", "learning_tests", "learning_tests_discrete", "fake_gpus"],
size = "large",
srcs = ["tests/run_regression_tests.py"],
data = ["tuned_examples/slateq/interest-evolution-10-candidates-recsim-env-slateq.yaml"],
args = ["--yaml-dir=tuned_examples/slateq"]
)
# TD3
# py_test(
# name = "learning_tests_pendulum_td3",
# main = "tests/run_regression_tests.py",
# tags = ["team:rllib", "exclusive", "learning_tests", "learning_tests_pendulum", "learning_tests_continuous"],
# size = "large",
# srcs = ["tests/run_regression_tests.py"],
# data = ["tuned_examples/ddpg/pendulum-td3.yaml"],
# args = ["--yaml-dir=tuned_examples/ddpg"]
# )
# --------------------------------------------------------------------
# Agents (Compilation, Losses, simple agent functionality tests)
# rllib/algorithms/
#
# Tag: algorithms_dir
# --------------------------------------------------------------------
# Generic (all Trainers)
py_test(
name = "test_callbacks",
tags = ["team:rllib", "algorithms_dir", "algorithms_dir_generic"],
size = "medium",
srcs = ["agents/tests/test_callbacks.py"]
)
py_test(
name = "test_memory_leaks_generic",
main = "agents/tests/test_memory_leaks.py",
tags = ["team:rllib", "algorithms_dir"],
size = "large",
srcs = ["agents/tests/test_memory_leaks.py"]
)
py_test(
name = "test_trainer",
tags = ["team:rllib", "algorithms_dir", "algorithms_dir_generic"],
size = "large",
srcs = ["agents/tests/test_trainer.py"]
)
py_test(
name = "tests/test_worker_failures",
tags = ["team:rllib", "tests_dir", "algorithms_dir_generic"],
size = "large",
srcs = ["agents/tests/test_worker_failures.py"]
)
# Specific Algorithms
# A2C
py_test(
name = "test_a2c",
tags = ["team:rllib", "algorithms_dir"],
size = "large",
srcs = ["algorithms/a2c/tests/test_a2c.py"]
)
# A3C
py_test(
name = "test_a3c",
tags = ["team:rllib", "algorithms_dir"],
size = "large",
srcs = ["algorithms/a3c/tests/test_a3c.py"]
)
# AlphaStar
py_test(
name = "test_alpha_star",
tags = ["team:rllib", "algorithms_dir"],
size = "large",
srcs = ["algorithms/alpha_star/tests/test_alpha_star.py"]
)
# AlphaZero
py_test(
name = "test_alpha_zero",
tags = ["team:rllib", "algorithms_dir"],
size = "large",
srcs = ["algorithms/alpha_zero/tests/test_alpha_zero.py"]
)
# APEX-DQN
py_test(
name = "test_apex_dqn",
tags = ["team:rllib", "algorithms_dir"],
size = "large",
srcs = ["algorithms/apex_dqn/tests/test_apex_dqn.py"]
)
# APEX-DDPG
py_test(
name = "test_apex_ddpg",
tags = ["team:rllib", "algorithms_dir"],
2020-07-11 22:06:35 +02:00
size = "medium",
srcs = ["algorithms/apex_ddpg/tests/test_apex_ddpg.py"]
)
# APPO
py_test(
name = "test_appo",
tags = ["team:rllib", "algorithms_dir"],
size = "large",
srcs = ["algorithms/appo/tests/test_appo.py"]
)
# ARS
py_test(
name = "test_ars",
tags = ["team:rllib", "algorithms_dir"],
size = "medium",
srcs = ["algorithms/ars/tests/test_ars.py"]
)
# Bandits
py_test(
name = "test_bandits",
tags = ["team:rllib", "algorithms_dir"],
size = "medium",
srcs = ["algorithms/bandit/tests/test_bandits.py"],
)
# BC
py_test(
name = "test_bc",
tags = ["team:rllib", "algorithms_dir"],
size = "large",
# Include the json data file.
data = ["tests/data/cartpole/large.json"],
srcs = ["algorithms/bc/tests/test_bc.py"]
)
# CQL
py_test(
name = "test_cql",
tags = ["team:rllib", "algorithms_dir"],
size = "medium",
srcs = ["algorithms/cql/tests/test_cql.py"]
)
2022-06-08 19:18:55 +02:00
# CRR
py_test(
name = "test_crr",
tags = ["team:rllib", "algorithms_dir"],
2022-06-08 19:18:55 +02:00
size = "medium",
srcs = ["algorithms/crr/tests/test_crr.py"]
)
# DDPG
py_test(
name = "test_ddpg",
tags = ["team:rllib", "algorithms_dir"],
size = "large",
srcs = ["algorithms/ddpg/tests/test_ddpg.py"]
)
# DDPPO
py_test(
name = "test_ddppo",
tags = ["team:rllib", "algorithms_dir"],
size = "medium",
srcs = ["algorithms/ddppo/tests/test_ddppo.py"]
)
# DQN
py_test(
name = "test_dqn",
tags = ["team:rllib", "algorithms_dir"],
size = "large",
srcs = ["algorithms/dqn/tests/test_dqn.py"]
)
# Dreamer
py_test(
name = "test_dreamer",
tags = ["team:rllib", "algorithms_dir"],
size = "medium",
srcs = ["algorithms/dreamer/tests/test_dreamer.py"]
)
# ES
py_test(
name = "test_es",
tags = ["team:rllib", "algorithms_dir"],
size = "medium",
srcs = ["algorithms/es/tests/test_es.py"]
)
# Impala
py_test(
name = "test_impala",
tags = ["team:rllib", "algorithms_dir"],
2020-07-11 22:06:35 +02:00
size = "large",
srcs = ["algorithms/impala/tests/test_impala.py"]
)
py_test(
name = "test_vtrace",
tags = ["team:rllib", "algorithms_dir"],
size = "small",
srcs = ["algorithms/impala/tests/test_vtrace.py"]
)
# MARWIL
py_test(
name = "test_marwil",
tags = ["team:rllib", "algorithms_dir"],
size = "large",
# Include the json data file.
data = ["tests/data/cartpole/large.json"],
srcs = ["algorithms/marwil/tests/test_marwil.py"]
)
# MADDPG
py_test(
name = "test_maddpg",
tags = ["team:rllib", "algorithms_dir"],
size = "medium",
srcs = ["algorithms/maddpg/tests/test_maddpg.py"]
)
# MAML
py_test(
name = "test_maml",
tags = ["team:rllib", "algorithms_dir"],
size = "medium",
srcs = ["algorithms/maml/tests/test_maml.py"]
)
# MBMPO
py_test(
name = "test_mbmpo",
tags = ["team:rllib", "algorithms_dir"],
size = "medium",
srcs = ["algorithms/mbmpo/tests/test_mbmpo.py"]
)
# PG
py_test(
name = "test_pg",
tags = ["team:rllib", "algorithms_dir"],
size = "large",
srcs = ["algorithms/pg/tests/test_pg.py"]
)
# PPO
py_test(
name = "test_ppo",
tags = ["team:rllib", "algorithms_dir"],
size = "large",
srcs = ["algorithms/ppo/tests/test_ppo.py"]
)
# QMix
py_test(
name = "test_qmix",
tags = ["team:rllib", "algorithms_dir"],
size = "medium",
srcs = ["algorithms/qmix/tests/test_qmix.py"]
)
# R2D2
py_test(
name = "test_r2d2",
tags = ["team:rllib", "algorithms_dir"],
size = "large",
srcs = ["algorithms/r2d2/tests/test_r2d2.py"]
)
# RNNSAC
py_test(
name = "test_rnnsac",
tags = ["team:rllib", "algorithms_dir"],
size = "medium",
srcs = ["algorithms/sac/tests/test_rnnsac.py"]
)
# SAC
py_test(
name = "test_sac",
tags = ["team:rllib", "algorithms_dir"],
size = "large",
srcs = ["algorithms/sac/tests/test_sac.py"]
)
[RLlib] SAC add discrete action support. (#7320) * Exploration API (+EpsilonGreedy sub-class). * Exploration API (+EpsilonGreedy sub-class). * Cleanup/LINT. * Add `deterministic` to generic Trainer config (NOTE: this is still ignored by most Agents). * Add `error` option to deprecation_warning(). * WIP. * Bug fix: Get exploration-info for tf framework. Bug fix: Properly deprecate some DQN config keys. * WIP. * LINT. * WIP. * Split PerWorkerEpsilonGreedy out of EpsilonGreedy. Docstrings. * Fix bug in sampler.py in case Policy has self.exploration = None * Update rllib/agents/dqn/dqn.py Co-Authored-By: Eric Liang <ekhliang@gmail.com> * WIP. * Update rllib/agents/trainer.py Co-Authored-By: Eric Liang <ekhliang@gmail.com> * WIP. * Change requests. * LINT * In tune/utils/util.py::deep_update() Only keep deep_updat'ing if both original and value are dicts. If value is not a dict, set * Completely obsolete syn_replay_optimizer.py's parameters schedule_max_timesteps AND beta_annealing_fraction (replaced with prioritized_replay_beta_annealing_timesteps). * Update rllib/evaluation/worker_set.py Co-Authored-By: Eric Liang <ekhliang@gmail.com> * Review fixes. * Fix default value for DQN's exploration spec. * LINT * Fix recursion bug (wrong parent c'tor). * Do not pass timestep to get_exploration_info. * Update tf_policy.py * Fix some remaining issues with test cases and remove more deprecated DQN/APEX exploration configs. * Bug fix tf-action-dist * DDPG incompatibility bug fix with new DQN exploration handling (which is imported by DDPG). * Switch off exploration when getting action probs from off-policy-estimator's policy. * LINT * Fix test_checkpoint_restore.py. * Deprecate all SAC exploration (unused) configs. * Properly use `model.last_output()` everywhere. Instead of `model._last_output`. * WIP. * Take out set_epsilon from multi-agent-env test (not needed, decays anyway). * WIP. * Trigger re-test (flaky checkpoint-restore test). * WIP. * WIP. * Add test case for deterministic action sampling in PPO. * bug fix. * Added deterministic test cases for different Agents. * Fix problem with TupleActions in dynamic-tf-policy. * Separate supported_spaces tests so they can be run separately for easier debugging. * LINT. * Fix autoregressive_action_dist.py test case. * Re-test. * Fix. * Remove duplicate py_test rule from bazel. * LINT. * WIP. * WIP. * SAC fix. * SAC fix. * WIP. * WIP. * WIP. * FIX 2 examples tests. * WIP. * WIP. * WIP. * WIP. * WIP. * Fix. * LINT. * Renamed test file. * WIP. * Add unittest.main. * Make action_dist_class mandatory. * fix * FIX. * WIP. * WIP. * Fix. * Fix. * Fix explorations test case (contextlib cannot find its own nullcontext??). * Force torch to be installed for QMIX. * LINT. * Fix determine_tests_to_run.py. * Fix determine_tests_to_run.py. * WIP * Add Random exploration component to tests (fixed issue with "static-graph randomness" via py_function). * Add Random exploration component to tests (fixed issue with "static-graph randomness" via py_function). * Rename some stuff. * Rename some stuff. * WIP. * update. * WIP. * Gumbel Softmax Dist. * WIP. * WIP. * WIP. * WIP. * WIP. * WIP. * WIP * WIP. * WIP. * Hypertune. * Hypertune. * Hypertune. * Lock-in. * Cleanup. * LINT. * Fix. * Update rllib/policy/eager_tf_policy.py Co-Authored-By: Kristian Hartikainen <kristian.hartikainen@gmail.com> * Update rllib/agents/sac/sac_policy.py Co-Authored-By: Kristian Hartikainen <kristian.hartikainen@gmail.com> * Update rllib/agents/sac/sac_policy.py Co-Authored-By: Kristian Hartikainen <kristian.hartikainen@gmail.com> * Update rllib/models/tf/tf_action_dist.py Co-Authored-By: Kristian Hartikainen <kristian.hartikainen@gmail.com> * Update rllib/models/tf/tf_action_dist.py Co-Authored-By: Kristian Hartikainen <kristian.hartikainen@gmail.com> * Fix items from review comments. * Add dm_tree to RLlib dependencies. * Add dm_tree to RLlib dependencies. * Fix DQN test cases ((Torch)Categorical). * Fix wrong pip install. Co-authored-by: Eric Liang <ekhliang@gmail.com> Co-authored-by: Kristian Hartikainen <kristian.hartikainen@gmail.com>
2020-03-06 19:37:12 +01:00
# SimpleQ
py_test(
name = "test_simple_q",
tags = ["team:rllib", "algorithms_dir"],
size = "medium",
srcs = ["algorithms/simple_q/tests/test_simple_q.py"]
)
# SlateQ
py_test(
name = "test_slateq",
tags = ["team:rllib", "algorithms_dir"],
size = "medium",
srcs = ["algorithms/slateq/tests/test_slateq.py"]
)
# TD3
py_test(
name = "test_td3",
tags = ["team:rllib", "algorithms_dir"],
size = "large",
srcs = ["algorithms/td3/tests/test_td3.py"]
)
# --------------------------------------------------------------------
# contrib Agents
# --------------------------------------------------------------------
py_test(
name = "random_agent",
tags = ["team:rllib", "algorithms_dir"],
main = "contrib/random_agent/random_agent.py",
size = "small",
srcs = ["contrib/random_agent/random_agent.py"]
)
# --------------------------------------------------------------------
# Memory leak tests
#
# Tag: memory_leak_tests
# --------------------------------------------------------------------
py_test(
name = "test_memory_leak_a3c",
tags = ["team:rllib", "memory_leak_tests"],
main = "utils/tests/run_memory_leak_tests.py",
size = "large",
srcs = ["utils/tests/run_memory_leak_tests.py"],
data = ["tuned_examples/a3c/memory-leak-test-a3c.yaml"],
args = ["--yaml-dir=tuned_examples/a3c"]
)
py_test(
name = "test_memory_leak_appo",
tags = ["team:rllib", "memory_leak_tests"],
main = "utils/tests/run_memory_leak_tests.py",
size = "large",
srcs = ["utils/tests/run_memory_leak_tests.py"],
data = ["tuned_examples/appo/memory-leak-test-appo.yaml"],
args = ["--yaml-dir=tuned_examples/appo"]
)
py_test(
name = "test_memory_leak_ddpg",
tags = ["team:rllib", "memory_leak_tests"],
main = "utils/tests/run_memory_leak_tests.py",
size = "large",
srcs = ["utils/tests/run_memory_leak_tests.py"],
data = ["tuned_examples/ddpg/memory-leak-test-ddpg.yaml"],
args = ["--yaml-dir=tuned_examples/ddpg"]
)
py_test(
name = "test_memory_leak_dqn",
tags = ["team:rllib", "memory_leak_tests"],
main = "utils/tests/run_memory_leak_tests.py",
size = "large",
srcs = ["utils/tests/run_memory_leak_tests.py"],
data = ["tuned_examples/dqn/memory-leak-test-dqn.yaml"],
args = ["--yaml-dir=tuned_examples/dqn"]
)
py_test(
name = "test_memory_leak_impala",
tags = ["team:rllib", "memory_leak_tests"],
main = "utils/tests/run_memory_leak_tests.py",
size = "large",
srcs = ["utils/tests/run_memory_leak_tests.py"],
data = ["tuned_examples/impala/memory-leak-test-impala.yaml"],
args = ["--yaml-dir=tuned_examples/impala"]
)
py_test(
name = "test_memory_leak_ppo",
tags = ["team:rllib", "memory_leak_tests"],
main = "utils/tests/run_memory_leak_tests.py",
size = "large",
srcs = ["utils/tests/run_memory_leak_tests.py"],
data = ["tuned_examples/ppo/memory-leak-test-ppo.yaml"],
args = ["--yaml-dir=tuned_examples/ppo"]
)
py_test(
name = "test_memory_leak_sac",
tags = ["team:rllib", "memory_leak_tests"],
main = "utils/tests/run_memory_leak_tests.py",
size = "large",
srcs = ["utils/tests/run_memory_leak_tests.py"],
data = ["tuned_examples/sac/memory-leak-test-sac.yaml"],
args = ["--yaml-dir=tuned_examples/sac"]
)
# --------------------------------------------------------------------
# Agents (quick training test iterations via `rllib train`)
#
# Tag: quick_train
#
# These are not(!) learning tests, we only test here compilation and
# support for certain envs, spaces, setups.
# Should all be very short tests with label: "quick_train".
# --------------------------------------------------------------------
# A2C/A3C
py_test(
name = "test_a3c_torch_pong_deterministic_v4",
main = "train.py", srcs = ["train.py"],
tags = ["team:rllib", "quick_train"],
args = [
"--env", "PongDeterministic-v4",
"--run", "A3C",
"--stop", "'{\"training_iteration\": 1}'",
"--config", "'{\"framework\": \"torch\", \"num_workers\": 2, \"sample_async\": false, \"model\": {\"use_lstm\": false, \"grayscale\": true, \"zero_mean\": false, \"dim\": 84}, \"preprocessor_pref\": \"rllib\"}'",
"--ray-num-cpus", "4"
]
)
py_test(
name = "test_a3c_tf_pong_ram_v4",
main = "train.py", srcs = ["train.py"],
tags = ["team:rllib", "quick_train"],
args = [
"--env", "Pong-ram-v4",
"--run", "A3C",
"--stop", "'{\"training_iteration\": 1}'",
"--config", "'{\"framework\": \"tf\", \"num_workers\": 2}'",
"--ray-num-cpus", "4"
]
)
# DDPG/APEX-DDPG/TD3
py_test(
name = "test_ddpg_mountaincar_continuous_v0_num_workers_0",
main = "train.py", srcs = ["train.py"],
tags = ["team:rllib", "quick_train"],
args = [
"--env", "MountainCarContinuous-v0",
"--run", "DDPG",
"--stop", "'{\"training_iteration\": 1}'",
"--config", "'{\"framework\": \"tf\", \"num_workers\": 0}'"
]
)
py_test(
name = "test_ddpg_mountaincar_continuous_v0_num_workers_1",
main = "train.py", srcs = ["train.py"],
tags = ["team:rllib", "quick_train"],
args = [
"--env", "MountainCarContinuous-v0",
"--run", "DDPG",
"--stop", "'{\"training_iteration\": 1}'",
"--config", "'{\"framework\": \"tf\", \"num_workers\": 1}'"
]
)
py_test(
name = "test_apex_ddpg_pendulum_v0_complete_episode_batches",
main = "train.py", srcs = ["train.py"],
tags = ["team:rllib", "quick_train"],
args = [
[RLlib] Upgrade gym version to 0.21 and deprecate pendulum-v0. (#19535) * Fix QMix, SAC, and MADDPA too. * Unpin gym and deprecate pendulum v0 Many tests in rllib depended on pendulum v0, however in gym 0.21, pendulum v0 was deprecated in favor of pendulum v1. This may change reward thresholds, so will have to potentially rerun all of the pendulum v1 benchmarks, or use another environment in favor. The same applies to frozen lake v0 and frozen lake v1 Lastly, all of the RLlib tests and have been moved to python 3.7 * Add gym installation based on python version. Pin python<= 3.6 to gym 0.19 due to install issues with atari roms in gym 0.20 * Reformatting * Fixing tests * Move atari-py install conditional to req.txt * migrate to new ale install method * Fix QMix, SAC, and MADDPA too. * Unpin gym and deprecate pendulum v0 Many tests in rllib depended on pendulum v0, however in gym 0.21, pendulum v0 was deprecated in favor of pendulum v1. This may change reward thresholds, so will have to potentially rerun all of the pendulum v1 benchmarks, or use another environment in favor. The same applies to frozen lake v0 and frozen lake v1 Lastly, all of the RLlib tests and have been moved to python 3.7 * Add gym installation based on python version. Pin python<= 3.6 to gym 0.19 due to install issues with atari roms in gym 0.20 Move atari-py install conditional to req.txt migrate to new ale install method Make parametric_actions_cartpole return float32 actions/obs Adding type conversions if obs/actions don't match space Add utils to make elements match gym space dtypes Co-authored-by: Jun Gong <jungong@anyscale.com> Co-authored-by: sven1977 <svenmika1977@gmail.com>
2021-11-03 08:24:00 -07:00
"--env", "Pendulum-v1",
"--run", "APEX_DDPG",
"--stop", "'{\"training_iteration\": 1}'",
"--config", "'{\"framework\": \"tf\", \"num_workers\": 2, \"optimizer\": {\"num_replay_buffer_shards\": 1}, \"replay_buffer_config\": {\"learning_starts\": 100}, \"min_time_s_per_iteration\": 1, \"batch_mode\": \"complete_episodes\"}'",
"--ray-num-cpus", "4",
]
)
# DQN/APEX
py_test(
name = "test_dqn_frozenlake_v1",
main = "train.py", srcs = ["train.py"],
size = "small",
tags = ["team:rllib", "quick_train"],
args = [
[RLlib] Upgrade gym version to 0.21 and deprecate pendulum-v0. (#19535) * Fix QMix, SAC, and MADDPA too. * Unpin gym and deprecate pendulum v0 Many tests in rllib depended on pendulum v0, however in gym 0.21, pendulum v0 was deprecated in favor of pendulum v1. This may change reward thresholds, so will have to potentially rerun all of the pendulum v1 benchmarks, or use another environment in favor. The same applies to frozen lake v0 and frozen lake v1 Lastly, all of the RLlib tests and have been moved to python 3.7 * Add gym installation based on python version. Pin python<= 3.6 to gym 0.19 due to install issues with atari roms in gym 0.20 * Reformatting * Fixing tests * Move atari-py install conditional to req.txt * migrate to new ale install method * Fix QMix, SAC, and MADDPA too. * Unpin gym and deprecate pendulum v0 Many tests in rllib depended on pendulum v0, however in gym 0.21, pendulum v0 was deprecated in favor of pendulum v1. This may change reward thresholds, so will have to potentially rerun all of the pendulum v1 benchmarks, or use another environment in favor. The same applies to frozen lake v0 and frozen lake v1 Lastly, all of the RLlib tests and have been moved to python 3.7 * Add gym installation based on python version. Pin python<= 3.6 to gym 0.19 due to install issues with atari roms in gym 0.20 Move atari-py install conditional to req.txt migrate to new ale install method Make parametric_actions_cartpole return float32 actions/obs Adding type conversions if obs/actions don't match space Add utils to make elements match gym space dtypes Co-authored-by: Jun Gong <jungong@anyscale.com> Co-authored-by: sven1977 <svenmika1977@gmail.com>
2021-11-03 08:24:00 -07:00
"--env", "FrozenLake-v1",
"--run", "DQN",
"--config", "'{\"framework\": \"tf\"}'",
"--stop", "'{\"training_iteration\": 1}'"
]
)
py_test(
name = "test_dqn_cartpole_v0_no_dueling",
main = "train.py", srcs = ["train.py"],
size = "small",
tags = ["team:rllib", "quick_train"],
args = [
"--env", "CartPole-v0",
"--run", "DQN",
"--stop", "'{\"training_iteration\": 1}'",
"--config", "'{\"framework\": \"tf\", \"lr\": 1e-3, \"exploration_config\": {\"epsilon_timesteps\": 10000, \"final_epsilon\": 0.02}, \"dueling\": false, \"hiddens\": [], \"model\": {\"fcnet_hiddens\": [64], \"fcnet_activation\": \"relu\"}}'"
]
)
py_test(
name = "test_dqn_cartpole_v0",
main = "train.py", srcs = ["train.py"],
tags = ["team:rllib", "quick_train"],
args = [
"--env", "CartPole-v0",
"--run", "DQN",
"--stop", "'{\"training_iteration\": 1}'",
"--config", "'{\"framework\": \"tf\", \"num_workers\": 2}'",
"--ray-num-cpus", "4"
]
)
py_test(
name = "test_dqn_cartpole_v0_with_offline_input_and_softq",
main = "train.py", srcs = ["train.py"],
tags = ["team:rllib", "quick_train", "external_files"],
size = "small",
# Include the json data file.
data = ["tests/data/cartpole/small.json"],
args = [
"--env", "CartPole-v0",
"--run", "DQN",
"--stop", "'{\"training_iteration\": 1}'",
"--config", "'{\"framework\": \"tf\", \"input\": \"tests/data/cartpole\", \"replay_buffer_config\": {\"learning_starts\": 0}, \"off_policy_estimation_methods\": {\"wis\": {\"type\": \"wis\"}, \"is\": {\"type\": \"is\"}}, \"exploration_config\": {\"type\": \"SoftQ\"}}'"
]
)
py_test(
name = "test_dqn_pong_deterministic_v4",
main = "train.py", srcs = ["train.py"],
tags = ["team:rllib", "quick_train"],
args = [
"--env", "PongDeterministic-v4",
"--run", "DQN",
"--stop", "'{\"training_iteration\": 1}'",
"--config", "'{\"framework\": \"tf\", \"lr\": 1e-4, \"exploration_config\": {\"epsilon_timesteps\": 200000, \"final_epsilon\": 0.01}, \"replay_buffer_config\": {\"capacity\": 10000, \"learning_starts\": 10000}, \"rollout_fragment_length\": 4, \"target_network_update_freq\": 1000, \"gamma\": 0.99}'"
]
)
# IMPALA
py_test(
name = "test_impala_buffers_2",
main = "train.py", srcs = ["train.py"],
tags = ["team:rllib", "quick_train"],
args = [
"--env", "CartPole-v0",
"--run", "IMPALA",
"--stop", "'{\"training_iteration\": 1}'",
"--config", "'{\"framework\": \"tf\", \"num_gpus\": 0, \"num_workers\": 2, \"min_time_s_per_iteration\": 1, \"num_multi_gpu_tower_stacks\": 2, \"replay_buffer_num_slots\": 100, \"replay_proportion\": 1.0}'",
"--ray-num-cpus", "4",
]
)
py_test(
name = "test_impala_cartpole_v0_buffers_2_lstm",
main = "train.py",
srcs = ["train.py"],
tags = ["team:rllib", "quick_train"],
args = [
"--env", "CartPole-v0",
"--run", "IMPALA",
"--stop", "'{\"training_iteration\": 1}'",
"--config", "'{\"framework\": \"tf\", \"num_gpus\": 0, \"num_workers\": 2, \"min_time_s_per_iteration\": 1, \"num_multi_gpu_tower_stacks\": 2, \"replay_buffer_num_slots\": 100, \"replay_proportion\": 1.0, \"model\": {\"use_lstm\": true}}'",
"--ray-num-cpus", "4",
]
)
py_test(
name = "test_impala_pong_deterministic_v4_40k_ts_1G_obj_store",
main = "train.py",
srcs = ["train.py"],
tags = ["team:rllib", "quick_train"],
size = "medium",
args = [
"--env", "PongDeterministic-v4",
"--run", "IMPALA",
"--stop", "'{\"timesteps_total\": 30000}'",
"--ray-object-store-memory=1000000000",
"--config", "'{\"framework\": \"tf\", \"num_workers\": 1, \"num_gpus\": 0, \"num_envs_per_worker\": 32, \"rollout_fragment_length\": 50, \"train_batch_size\": 50, \"learner_queue_size\": 1}'"
]
)
# PG
py_test(
name = "test_pg_tf_cartpole_v0_lstm",
main = "train.py", srcs = ["train.py"],
tags = ["team:rllib", "quick_train"],
args = [
"--env", "CartPole-v0",
"--run", "PG",
"--stop", "'{\"training_iteration\": 1}'",
"--config", "'{\"framework\": \"tf\", \"rollout_fragment_length\": 500, \"num_workers\": 1, \"model\": {\"use_lstm\": true, \"max_seq_len\": 100}}'"
]
)
py_test(
name = "test_pg_tf_cartpole_v0_multi_envs_per_worker",
main = "train.py", srcs = ["train.py"],
size = "small",
tags = ["team:rllib", "quick_train"],
args = [
"--env", "CartPole-v0",
"--run", "PG",
"--stop", "'{\"training_iteration\": 1}'",
"--config", "'{\"framework\": \"tf\", \"rollout_fragment_length\": 500, \"num_workers\": 1, \"num_envs_per_worker\": 10}'"
]
)
py_test(
name = "test_pg_tf_pong_v0",
main = "train.py", srcs = ["train.py"],
tags = ["team:rllib", "quick_train"],
args = [
"--env", "Pong-v0",
"--run", "PG",
"--stop", "'{\"training_iteration\": 1}'",
"--config", "'{\"framework\": \"tf\", \"rollout_fragment_length\": 500, \"num_workers\": 1}'"
]
)
# PPO/APPO
py_test(
name = "test_ppo_tf_cartpole_v1_complete_episode_batches",
main = "train.py", srcs = ["train.py"],
tags = ["team:rllib", "quick_train"],
args = [
"--env", "CartPole-v1",
"--run", "PPO",
"--stop", "'{\"training_iteration\": 1}'",
"--config", "'{\"framework\": \"tf\", \"kl_coeff\": 1.0, \"num_sgd_iter\": 10, \"lr\": 1e-4, \"sgd_minibatch_size\": 64, \"train_batch_size\": 2000, \"num_workers\": 1, \"use_gae\": false, \"batch_mode\": \"complete_episodes\"}'"
]
)
py_test(
name = "test_ppo_tf_cartpole_v1_remote_worker_envs",
main = "train.py", srcs = ["train.py"],
tags = ["team:rllib", "quick_train"],
args = [
"--env", "CartPole-v1",
"--run", "PPO",
"--stop", "'{\"training_iteration\": 1}'",
"--config", "'{\"framework\": \"tf\", \"remote_worker_envs\": true, \"remote_env_batch_wait_ms\": 99999999, \"num_envs_per_worker\": 2, \"num_workers\": 1, \"train_batch_size\": 100, \"sgd_minibatch_size\": 50}'"
]
)
py_test(
name = "test_ppo_tf_cartpole_v1_remote_worker_envs_b",
main = "train.py", srcs = ["train.py"],
tags = ["team:rllib", "quick_train"],
args = [
"--env", "CartPole-v1",
"--run", "PPO",
"--stop", "'{\"training_iteration\": 2}'",
"--config", "'{\"framework\": \"tf\", \"remote_worker_envs\": true, \"num_envs_per_worker\": 2, \"num_workers\": 1, \"train_batch_size\": 100, \"sgd_minibatch_size\": 50}'"
]
)
py_test(
name = "test_appo_tf_pendulum_v1_no_gpus",
main = "train.py", srcs = ["train.py"],
tags = ["team:rllib", "quick_train"],
args = [
[RLlib] Upgrade gym version to 0.21 and deprecate pendulum-v0. (#19535) * Fix QMix, SAC, and MADDPA too. * Unpin gym and deprecate pendulum v0 Many tests in rllib depended on pendulum v0, however in gym 0.21, pendulum v0 was deprecated in favor of pendulum v1. This may change reward thresholds, so will have to potentially rerun all of the pendulum v1 benchmarks, or use another environment in favor. The same applies to frozen lake v0 and frozen lake v1 Lastly, all of the RLlib tests and have been moved to python 3.7 * Add gym installation based on python version. Pin python<= 3.6 to gym 0.19 due to install issues with atari roms in gym 0.20 * Reformatting * Fixing tests * Move atari-py install conditional to req.txt * migrate to new ale install method * Fix QMix, SAC, and MADDPA too. * Unpin gym and deprecate pendulum v0 Many tests in rllib depended on pendulum v0, however in gym 0.21, pendulum v0 was deprecated in favor of pendulum v1. This may change reward thresholds, so will have to potentially rerun all of the pendulum v1 benchmarks, or use another environment in favor. The same applies to frozen lake v0 and frozen lake v1 Lastly, all of the RLlib tests and have been moved to python 3.7 * Add gym installation based on python version. Pin python<= 3.6 to gym 0.19 due to install issues with atari roms in gym 0.20 Move atari-py install conditional to req.txt migrate to new ale install method Make parametric_actions_cartpole return float32 actions/obs Adding type conversions if obs/actions don't match space Add utils to make elements match gym space dtypes Co-authored-by: Jun Gong <jungong@anyscale.com> Co-authored-by: sven1977 <svenmika1977@gmail.com>
2021-11-03 08:24:00 -07:00
"--env", "Pendulum-v1",
"--run", "APPO",
"--stop", "'{\"training_iteration\": 1}'",
"--config", "'{\"framework\": \"tf\", \"num_workers\": 2, \"num_gpus\": 0}'",
"--ray-num-cpus", "4"
]
)
# --------------------------------------------------------------------
# Connector tests
# rllib/connector/
#
# Tag: connector
# --------------------------------------------------------------------
py_test(
name = "test_connector",
tags = ["team:rllib", "connector"],
size = "small",
srcs = ["connectors/tests/test_connector.py"]
)
py_test(
name = "test_action",
tags = ["team:rllib", "connector"],
size = "small",
srcs = ["connectors/tests/test_action.py"]
)
py_test(
name = "test_agent",
tags = ["team:rllib", "connector"],
size = "small",
srcs = ["connectors/tests/test_agent.py"]
)
# --------------------------------------------------------------------
# Env tests
# rllib/env/
#
# Tag: env
# --------------------------------------------------------------------
py_test(
name = "env/tests/test_external_env",
tags = ["team:rllib", "env"],
size = "large",
srcs = ["env/tests/test_external_env.py"]
)
py_test(
name = "env/tests/test_external_multi_agent_env",
tags = ["team:rllib", "env"],
size = "medium",
srcs = ["env/tests/test_external_multi_agent_env.py"]
)
sh_test(
name = "env/tests/test_local_inference_cartpole",
tags = ["team:rllib", "env"],
size = "medium",
srcs = ["env/tests/test_policy_client_server_setup.sh"],
args = ["local", "cartpole", "8800"],
data = glob(["examples/serving/*.py"]),
)
sh_test(
name = "env/tests/test_remote_inference_cartpole",
tags = ["team:rllib", "env"],
size = "medium",
srcs = ["env/tests/test_policy_client_server_setup.sh"],
args = ["remote", "cartpole", "8810"],
data = glob(["examples/serving/*.py"]),
)
sh_test(
name = "env/tests/test_remote_inference_cartpole_lstm",
tags = ["team:rllib", "env"],
size = "large",
srcs = ["env/tests/test_policy_client_server_setup.sh"],
args = ["remote", "cartpole_lstm", "8820"],
data = glob(["examples/serving/*.py"]),
)
sh_test(
name = "env/tests/test_local_inference_cartpole_w_2_concurrent_episodes",
tags = ["team:rllib", "env"],
size = "medium",
srcs = ["env/tests/test_policy_client_server_setup.sh"],
args = ["local", "cartpole-dummy-2-episodes", "8830"],
data = glob(["examples/serving/*.py"]),
)
sh_test(
name = "env/tests/test_remote_inference_cartpole_w_2_concurrent_episodes",
tags = ["team:rllib", "env"],
size = "medium",
srcs = ["env/tests/test_policy_client_server_setup.sh"],
args = ["remote", "cartpole-dummy-2-episodes", "8840"],
data = glob(["examples/serving/*.py"]),
)
sh_test(
name = "env/tests/test_local_inference_unity3d",
tags = ["team:rllib", "env"],
size = "medium",
srcs = ["env/tests/test_policy_client_server_setup.sh"],
args = ["local", "unity3d", "8850"],
data = glob(["examples/serving/*.py"]),
)
sh_test(
name = "env/tests/test_remote_inference_unity3d",
tags = ["team:rllib", "env"],
size = "medium",
srcs = ["env/tests/test_policy_client_server_setup.sh"],
args = ["remote", "unity3d", "8860"],
data = glob(["examples/serving/*.py"]),
)
py_test(
name = "env/tests/test_remote_worker_envs",
tags = ["team:rllib", "env"],
size = "medium",
srcs = ["env/tests/test_remote_worker_envs.py"]
)
py_test(
name = "env/tests/test_env_with_subprocess",
tags = ["team:rllib", "env"],
size = "medium",
srcs = ["env/tests/test_env_with_subprocess.py"]
)
py_test(
name = "env/wrappers/tests/test_unity3d_env",
tags = ["team:rllib", "env"],
size = "small",
srcs = ["env/wrappers/tests/test_unity3d_env.py"]
)
py_test(
name = "env/wrappers/tests/test_recsim_wrapper",
tags = ["team:rllib", "env"],
size = "small",
srcs = ["env/wrappers/tests/test_recsim_wrapper.py"]
)
py_test(
name = "env/wrappers/tests/test_exception_wrapper",
tags = ["team:rllib", "env"],
size = "small",
srcs = ["env/wrappers/tests/test_exception_wrapper.py"]
)
py_test(
name = "env/wrappers/tests/test_group_agents_wrapper",
tags = ["team:rllib", "env"],
size = "small",
srcs = ["env/wrappers/tests/test_group_agents_wrapper.py"]
)
# --------------------------------------------------------------------
# Evaluation components
# rllib/evaluation/
#
# Tag: evaluation
# --------------------------------------------------------------------
py_test(
name = "evaluation/tests/test_envs_that_crash",
tags = ["team:rllib", "evaluation"],
size = "medium",
srcs = ["evaluation/tests/test_envs_that_crash.py"]
)
py_test(
name = "evaluation/tests/test_episode",
tags = ["team:rllib", "evaluation"],
size = "small",
srcs = ["evaluation/tests/test_episode.py"]
)
py_test(
name = "evaluation/tests/test_postprocessing",
tags = ["team:rllib", "evaluation"],
size = "small",
srcs = ["evaluation/tests/test_postprocessing.py"]
)
py_test(
name = "evaluation/tests/test_rollout_worker",
tags = ["team:rllib", "evaluation"],
size = "medium",
srcs = ["evaluation/tests/test_rollout_worker.py"]
)
py_test(
name = "evaluation/tests/test_trajectory_view_api",
tags = ["team:rllib", "evaluation"],
size = "medium",
srcs = ["evaluation/tests/test_trajectory_view_api.py"]
)
# --------------------------------------------------------------------
# Execution Utils
# rllib/execution/
#
# Tag: execution
# --------------------------------------------------------------------
py_test(
name = "test_async_requests_manager",
tags = ["team:rllib", "execution"],
size = "small",
srcs = ["execution/tests/test_async_requests_manager.py"]
)
# --------------------------------------------------------------------
# Models and Distributions
# rllib/models/
#
# Tag: models
# --------------------------------------------------------------------
py_test(
name = "test_attention_nets",
tags = ["team:rllib", "models"],
size = "large",
srcs = ["models/tests/test_attention_nets.py"]
)
py_test(
name = "test_conv2d_default_stacks",
tags = ["team:rllib", "models"],
size = "medium",
srcs = ["models/tests/test_conv2d_default_stacks.py"]
)
py_test(
name = "test_convtranspose2d_stack",
tags = ["team:rllib", "models"],
size = "small",
data = glob(["tests/data/images/obstacle_tower.png"]),
srcs = ["models/tests/test_convtranspose2d_stack.py"]
)
py_test(
name = "test_distributions",
tags = ["team:rllib", "models"],
size = "medium",
srcs = ["models/tests/test_distributions.py"]
)
py_test(
name = "test_lstms",
tags = ["team:rllib", "models"],
size = "large",
srcs = ["models/tests/test_lstms.py"]
)
py_test(
name = "test_models",
tags = ["team:rllib", "models"],
size = "medium",
srcs = ["models/tests/test_models.py"]
)
py_test(
name = "test_preprocessors",
tags = ["team:rllib", "models"],
size = "large",
srcs = ["models/tests/test_preprocessors.py"]
)
# --------------------------------------------------------------------
# Offline
# rllib/offline/
#
# Tag: offline
# --------------------------------------------------------------------
py_test(
name = "test_ope",
tags = ["team:rllib", "offline", "torch_only"],
size = "medium",
srcs = ["offline/estimators/tests/test_ope.py"]
)
[RLlib] Policy.compute_log_likelihoods() and SAC refactor. (issue #7107) (#7124) * Exploration API (+EpsilonGreedy sub-class). * Exploration API (+EpsilonGreedy sub-class). * Cleanup/LINT. * Add `deterministic` to generic Trainer config (NOTE: this is still ignored by most Agents). * Add `error` option to deprecation_warning(). * WIP. * Bug fix: Get exploration-info for tf framework. Bug fix: Properly deprecate some DQN config keys. * WIP. * LINT. * WIP. * Split PerWorkerEpsilonGreedy out of EpsilonGreedy. Docstrings. * Fix bug in sampler.py in case Policy has self.exploration = None * Update rllib/agents/dqn/dqn.py Co-Authored-By: Eric Liang <ekhliang@gmail.com> * WIP. * Update rllib/agents/trainer.py Co-Authored-By: Eric Liang <ekhliang@gmail.com> * WIP. * Change requests. * LINT * In tune/utils/util.py::deep_update() Only keep deep_updat'ing if both original and value are dicts. If value is not a dict, set * Completely obsolete syn_replay_optimizer.py's parameters schedule_max_timesteps AND beta_annealing_fraction (replaced with prioritized_replay_beta_annealing_timesteps). * Update rllib/evaluation/worker_set.py Co-Authored-By: Eric Liang <ekhliang@gmail.com> * Review fixes. * Fix default value for DQN's exploration spec. * LINT * Fix recursion bug (wrong parent c'tor). * Do not pass timestep to get_exploration_info. * Update tf_policy.py * Fix some remaining issues with test cases and remove more deprecated DQN/APEX exploration configs. * Bug fix tf-action-dist * DDPG incompatibility bug fix with new DQN exploration handling (which is imported by DDPG). * Switch off exploration when getting action probs from off-policy-estimator's policy. * LINT * Fix test_checkpoint_restore.py. * Deprecate all SAC exploration (unused) configs. * Properly use `model.last_output()` everywhere. Instead of `model._last_output`. * WIP. * Take out set_epsilon from multi-agent-env test (not needed, decays anyway). * WIP. * Trigger re-test (flaky checkpoint-restore test). * WIP. * WIP. * Add test case for deterministic action sampling in PPO. * bug fix. * Added deterministic test cases for different Agents. * Fix problem with TupleActions in dynamic-tf-policy. * Separate supported_spaces tests so they can be run separately for easier debugging. * LINT. * Fix autoregressive_action_dist.py test case. * Re-test. * Fix. * Remove duplicate py_test rule from bazel. * LINT. * WIP. * WIP. * SAC fix. * SAC fix. * WIP. * WIP. * WIP. * FIX 2 examples tests. * WIP. * WIP. * WIP. * WIP. * WIP. * Fix. * LINT. * Renamed test file. * WIP. * Add unittest.main. * Make action_dist_class mandatory. * fix * FIX. * WIP. * WIP. * Fix. * Fix. * Fix explorations test case (contextlib cannot find its own nullcontext??). * Force torch to be installed for QMIX. * LINT. * Fix determine_tests_to_run.py. * Fix determine_tests_to_run.py. * WIP * Add Random exploration component to tests (fixed issue with "static-graph randomness" via py_function). * Add Random exploration component to tests (fixed issue with "static-graph randomness" via py_function). * Rename some stuff. * Rename some stuff. * WIP. * WIP. * Fix SAC. * Fix SAC. * Fix strange tf-error in ray core tests. * Fix strange ray-core tf-error in test_memory_scheduling test case. * Fix test_io.py. * LINT. * Update SAC yaml files' config. Co-authored-by: Eric Liang <ekhliang@gmail.com>
2020-02-22 23:19:49 +01:00
# --------------------------------------------------------------------
# Policies
# rllib/policy/
#
# Tag: policy
# --------------------------------------------------------------------
py_test(
name = "policy/tests/test_compute_log_likelihoods",
tags = ["team:rllib", "policy"],
size = "medium",
srcs = ["policy/tests/test_compute_log_likelihoods.py"]
)
[RLlib] Policy.compute_log_likelihoods() and SAC refactor. (issue #7107) (#7124) * Exploration API (+EpsilonGreedy sub-class). * Exploration API (+EpsilonGreedy sub-class). * Cleanup/LINT. * Add `deterministic` to generic Trainer config (NOTE: this is still ignored by most Agents). * Add `error` option to deprecation_warning(). * WIP. * Bug fix: Get exploration-info for tf framework. Bug fix: Properly deprecate some DQN config keys. * WIP. * LINT. * WIP. * Split PerWorkerEpsilonGreedy out of EpsilonGreedy. Docstrings. * Fix bug in sampler.py in case Policy has self.exploration = None * Update rllib/agents/dqn/dqn.py Co-Authored-By: Eric Liang <ekhliang@gmail.com> * WIP. * Update rllib/agents/trainer.py Co-Authored-By: Eric Liang <ekhliang@gmail.com> * WIP. * Change requests. * LINT * In tune/utils/util.py::deep_update() Only keep deep_updat'ing if both original and value are dicts. If value is not a dict, set * Completely obsolete syn_replay_optimizer.py's parameters schedule_max_timesteps AND beta_annealing_fraction (replaced with prioritized_replay_beta_annealing_timesteps). * Update rllib/evaluation/worker_set.py Co-Authored-By: Eric Liang <ekhliang@gmail.com> * Review fixes. * Fix default value for DQN's exploration spec. * LINT * Fix recursion bug (wrong parent c'tor). * Do not pass timestep to get_exploration_info. * Update tf_policy.py * Fix some remaining issues with test cases and remove more deprecated DQN/APEX exploration configs. * Bug fix tf-action-dist * DDPG incompatibility bug fix with new DQN exploration handling (which is imported by DDPG). * Switch off exploration when getting action probs from off-policy-estimator's policy. * LINT * Fix test_checkpoint_restore.py. * Deprecate all SAC exploration (unused) configs. * Properly use `model.last_output()` everywhere. Instead of `model._last_output`. * WIP. * Take out set_epsilon from multi-agent-env test (not needed, decays anyway). * WIP. * Trigger re-test (flaky checkpoint-restore test). * WIP. * WIP. * Add test case for deterministic action sampling in PPO. * bug fix. * Added deterministic test cases for different Agents. * Fix problem with TupleActions in dynamic-tf-policy. * Separate supported_spaces tests so they can be run separately for easier debugging. * LINT. * Fix autoregressive_action_dist.py test case. * Re-test. * Fix. * Remove duplicate py_test rule from bazel. * LINT. * WIP. * WIP. * SAC fix. * SAC fix. * WIP. * WIP. * WIP. * FIX 2 examples tests. * WIP. * WIP. * WIP. * WIP. * WIP. * Fix. * LINT. * Renamed test file. * WIP. * Add unittest.main. * Make action_dist_class mandatory. * fix * FIX. * WIP. * WIP. * Fix. * Fix. * Fix explorations test case (contextlib cannot find its own nullcontext??). * Force torch to be installed for QMIX. * LINT. * Fix determine_tests_to_run.py. * Fix determine_tests_to_run.py. * WIP * Add Random exploration component to tests (fixed issue with "static-graph randomness" via py_function). * Add Random exploration component to tests (fixed issue with "static-graph randomness" via py_function). * Rename some stuff. * Rename some stuff. * WIP. * WIP. * Fix SAC. * Fix SAC. * Fix strange tf-error in ray core tests. * Fix strange ray-core tf-error in test_memory_scheduling test case. * Fix test_io.py. * LINT. * Update SAC yaml files' config. Co-authored-by: Eric Liang <ekhliang@gmail.com>
2020-02-22 23:19:49 +01:00
py_test(
name = "policy/tests/test_multi_agent_batch",
tags = ["team:rllib", "policy"],
size = "small",
srcs = ["policy/tests/test_multi_agent_batch.py"]
)
py_test(
name = "policy/tests/test_policy",
tags = ["team:rllib", "policy"],
size = "medium",
srcs = ["policy/tests/test_policy.py"]
)
py_test(
name = "policy/tests/test_rnn_sequencing",
tags = ["team:rllib", "policy"],
size = "small",
srcs = ["policy/tests/test_rnn_sequencing.py"]
)
py_test(
name = "policy/tests/test_sample_batch",
tags = ["team:rllib", "policy"],
size = "small",
srcs = ["policy/tests/test_sample_batch.py"]
)
py_test(
name = "policy/tests/test_view_requirement",
tags = ["team:rllib", "policy"],
size = "small",
srcs = ["policy/tests/test_view_requirement.py"]
)
# --------------------------------------------------------------------
# Utils:
# rllib/utils/
#
# Tag: utils
# --------------------------------------------------------------------
py_test(
name = "test_serialization",
tags = ["team:rllib", "utils"],
size = "large",
srcs = ["utils/tests/test_serialization.py"]
)
py_test(
name = "test_curiosity",
tags = ["team:rllib", "utils"],
size = "large",
srcs = ["utils/exploration/tests/test_curiosity.py"]
)
py_test(
name = "test_explorations",
tags = ["team:rllib", "utils"],
size = "large",
srcs = ["utils/exploration/tests/test_explorations.py"]
)
py_test(
name = "test_parameter_noise",
tags = ["team:rllib", "utils"],
size = "medium",
srcs = ["utils/exploration/tests/test_parameter_noise.py"]
)
py_test(
name = "test_random_encoder",
tags = ["team:rllib", "utils"],
size = "large",
srcs = ["utils/exploration/tests/test_random_encoder.py"]
)
# Schedules
py_test(
name = "test_schedules",
tags = ["team:rllib", "utils"],
size = "small",
srcs = ["utils/schedules/tests/test_schedules.py"]
)
py_test(
name = "test_framework_agnostic_components",
tags = ["team:rllib", "utils"],
size = "small",
data = glob(["utils/tests/**"]),
srcs = ["utils/tests/test_framework_agnostic_components.py"]
)
[RLlib] Upgrade gym version to 0.21 and deprecate pendulum-v0. (#19535) * Fix QMix, SAC, and MADDPA too. * Unpin gym and deprecate pendulum v0 Many tests in rllib depended on pendulum v0, however in gym 0.21, pendulum v0 was deprecated in favor of pendulum v1. This may change reward thresholds, so will have to potentially rerun all of the pendulum v1 benchmarks, or use another environment in favor. The same applies to frozen lake v0 and frozen lake v1 Lastly, all of the RLlib tests and have been moved to python 3.7 * Add gym installation based on python version. Pin python<= 3.6 to gym 0.19 due to install issues with atari roms in gym 0.20 * Reformatting * Fixing tests * Move atari-py install conditional to req.txt * migrate to new ale install method * Fix QMix, SAC, and MADDPA too. * Unpin gym and deprecate pendulum v0 Many tests in rllib depended on pendulum v0, however in gym 0.21, pendulum v0 was deprecated in favor of pendulum v1. This may change reward thresholds, so will have to potentially rerun all of the pendulum v1 benchmarks, or use another environment in favor. The same applies to frozen lake v0 and frozen lake v1 Lastly, all of the RLlib tests and have been moved to python 3.7 * Add gym installation based on python version. Pin python<= 3.6 to gym 0.19 due to install issues with atari roms in gym 0.20 Move atari-py install conditional to req.txt migrate to new ale install method Make parametric_actions_cartpole return float32 actions/obs Adding type conversions if obs/actions don't match space Add utils to make elements match gym space dtypes Co-authored-by: Jun Gong <jungong@anyscale.com> Co-authored-by: sven1977 <svenmika1977@gmail.com>
2021-11-03 08:24:00 -07:00
# Spaces/Space utils.
py_test(
name = "test_space_utils",
tags = ["team:rllib", "utils"],
[RLlib] Upgrade gym version to 0.21 and deprecate pendulum-v0. (#19535) * Fix QMix, SAC, and MADDPA too. * Unpin gym and deprecate pendulum v0 Many tests in rllib depended on pendulum v0, however in gym 0.21, pendulum v0 was deprecated in favor of pendulum v1. This may change reward thresholds, so will have to potentially rerun all of the pendulum v1 benchmarks, or use another environment in favor. The same applies to frozen lake v0 and frozen lake v1 Lastly, all of the RLlib tests and have been moved to python 3.7 * Add gym installation based on python version. Pin python<= 3.6 to gym 0.19 due to install issues with atari roms in gym 0.20 * Reformatting * Fixing tests * Move atari-py install conditional to req.txt * migrate to new ale install method * Fix QMix, SAC, and MADDPA too. * Unpin gym and deprecate pendulum v0 Many tests in rllib depended on pendulum v0, however in gym 0.21, pendulum v0 was deprecated in favor of pendulum v1. This may change reward thresholds, so will have to potentially rerun all of the pendulum v1 benchmarks, or use another environment in favor. The same applies to frozen lake v0 and frozen lake v1 Lastly, all of the RLlib tests and have been moved to python 3.7 * Add gym installation based on python version. Pin python<= 3.6 to gym 0.19 due to install issues with atari roms in gym 0.20 Move atari-py install conditional to req.txt migrate to new ale install method Make parametric_actions_cartpole return float32 actions/obs Adding type conversions if obs/actions don't match space Add utils to make elements match gym space dtypes Co-authored-by: Jun Gong <jungong@anyscale.com> Co-authored-by: sven1977 <svenmika1977@gmail.com>
2021-11-03 08:24:00 -07:00
size = "large",
srcs = ["utils/spaces/tests/test_space_utils.py"]
)
# TaskPool
py_test(
name = "test_taskpool",
tags = ["team:rllib", "utils"],
size = "small",
srcs = ["utils/tests/test_taskpool.py"]
)
# ReplayBuffers
py_test(
name = "test_multi_agent_mixin_replay_buffer",
tags = ["team:rllib", "utils"],
size = "small",
srcs = ["utils/replay_buffers/tests/test_multi_agent_mixin_replay_buffer.py"]
)
py_test(
name = "test_multi_agent_prioritized_replay_buffer",
tags = ["team:rllib", "utils"],
size = "small",
srcs = ["utils/replay_buffers/tests/test_multi_agent_prioritized_replay_buffer.py"]
)
py_test(
name = "test_multi_agent_replay_buffer",
tags = ["team:rllib", "utils"],
size = "small",
srcs = ["utils/replay_buffers/tests/test_multi_agent_replay_buffer.py"]
)
py_test(
name = "test_prioritized_replay_buffer_replay_buffer_api",
tags = ["team:rllib", "utils"],
size = "small",
srcs = ["utils/replay_buffers/tests/test_prioritized_replay_buffer_replay_buffer_api.py"]
)
py_test(
name = "test_replay_buffer",
tags = ["team:rllib", "utils"],
size = "small",
srcs = ["utils/replay_buffers/tests/test_replay_buffer.py"]
)
py_test(
name = "test_reservoir_buffer",
tags = ["team:rllib", "utils"],
size = "small",
srcs = ["utils/replay_buffers/tests/test_reservoir_buffer.py"]
)
py_test(
name = "test_segment_tree_replay_buffer_api",
tags = ["team:rllib", "utils"],
size = "small",
srcs = ["utils/replay_buffers/tests/test_segment_tree_replay_buffer_api.py"]
)
# --------------------------------------------------------------------
# rllib/tests/ directory
#
# Tag: tests_dir, tests_dir_[A-Z]
#
# NOTE: Add tests alphabetically into this list and make sure, to tag
# it correctly by its starting letter, e.g. tags=["tests_dir", "tests_dir_A"]
# for `tests/test_all_stuff.py`.
# --------------------------------------------------------------------
py_test(
name = "tests/backward_compat/test_backward_compat",
tags = ["team:rllib", "tests_dir", "tests_dir_B"],
size = "medium",
srcs = ["tests/backward_compat/test_backward_compat.py"]
)
py_test(
name = "tests/test_algorithm_imports",
tags = ["team:rllib", "tests_dir", "tests_dir_C"],
size = "small",
srcs = ["tests/test_algorithm_imports.py"]
)
py_test(
name = "tests/test_catalog",
tags = ["team:rllib", "tests_dir", "tests_dir_C"],
size = "medium",
srcs = ["tests/test_catalog.py"]
)
py_test(
name = "tests/test_checkpoint_restore_pg",
main = "tests/test_checkpoint_restore.py",
tags = ["team:rllib", "tests_dir", "tests_dir_C"],
size = "large",
srcs = ["tests/test_checkpoint_restore.py"],
args = ["TestCheckpointRestorePG"]
)
py_test(
name = "tests/test_checkpoint_restore_off_policy",
main = "tests/test_checkpoint_restore.py",
tags = ["team:rllib", "tests_dir", "tests_dir_C"],
size = "large",
srcs = ["tests/test_checkpoint_restore.py"],
args = ["TestCheckpointRestoreOffPolicy"]
)
py_test(
name = "tests/test_checkpoint_restore_evolution_algos",
main = "tests/test_checkpoint_restore.py",
tags = ["team:rllib", "tests_dir", "tests_dir_C"],
size = "large",
srcs = ["tests/test_checkpoint_restore.py"],
args = ["TestCheckpointRestoreEvolutionAlgos"]
)
py_test(
name = "tests/test_custom_resource",
tags = ["team:rllib", "tests_dir", "tests_dir_C"],
size = "medium",
srcs = ["tests/test_custom_resource.py"]
)
py_test(
name = "tests/test_dependency_tf",
tags = ["team:rllib", "tests_dir", "tests_dir_D"],
size = "small",
srcs = ["tests/test_dependency_tf.py"]
)
py_test(
name = "tests/test_dependency_torch",
tags = ["team:rllib", "tests_dir", "tests_dir_D"],
size = "small",
srcs = ["tests/test_dependency_torch.py"]
)
py_test(
name = "tests/test_eager_support_pg",
main = "tests/test_eager_support.py",
tags = ["team:rllib", "tests_dir", "tests_dir_E"],
size = "large",
srcs = ["tests/test_eager_support.py"],
args = ["TestEagerSupportPG"]
)
py_test(
name = "tests/test_eager_support_off_policy",
main = "tests/test_eager_support.py",
tags = ["team:rllib", "tests_dir", "tests_dir_E"],
size = "large",
srcs = ["tests/test_eager_support.py"],
args = ["TestEagerSupportOffPolicy"]
)
py_test(
name = "tests/test_execution",
tags = ["team:rllib", "tests_dir", "tests_dir_E"],
size = "medium",
srcs = ["tests/test_execution.py"]
)
py_test(
name = "tests/test_export",
tags = ["team:rllib", "tests_dir", "tests_dir_E"],
size = "medium",
srcs = ["tests/test_export.py"]
)
py_test(
name = "tests/test_filters",
tags = ["team:rllib", "tests_dir", "tests_dir_F"],
size = "small",
srcs = ["tests/test_filters.py"]
)
py_test(
name = "tests/test_gpus",
tags = ["team:rllib", "tests_dir", "tests_dir_G"],
size = "large",
srcs = ["tests/test_gpus.py"]
)
py_test(
name = "tests/test_io",
tags = ["team:rllib", "tests_dir", "tests_dir_I"],
size = "large",
srcs = ["tests/test_io.py"]
)
py_test(
name = "tests/test_local",
tags = ["team:rllib", "tests_dir", "tests_dir_L"],
size = "medium",
srcs = ["tests/test_local.py"]
)
py_test(
name = "tests/test_lstm",
tags = ["team:rllib", "tests_dir", "tests_dir_L"],
size = "medium",
srcs = ["tests/test_lstm.py"]
)
py_test(
name = "tests/test_model_imports",
tags = ["team:rllib", "tests_dir", "tests_dir_M", "model_imports"],
size = "medium",
data = glob(["tests/data/model_weights/**"]),
srcs = ["tests/test_model_imports.py"]
)
py_test(
name = "tests/test_multi_agent_env",
tags = ["team:rllib", "tests_dir", "tests_dir_M"],
size = "medium",
srcs = ["tests/test_multi_agent_env.py"]
)
py_test(
name = "tests/test_multi_agent_pendulum",
tags = ["team:rllib", "tests_dir", "tests_dir_M"],
size = "large",
srcs = ["tests/test_multi_agent_pendulum.py"]
)
py_test(
name = "tests/test_nested_action_spaces",
main = "tests/test_nested_action_spaces.py",
tags = ["team:rllib", "tests_dir", "tests_dir_N"],
size = "medium",
srcs = ["tests/test_nested_action_spaces.py"]
)
py_test(
name = "tests/test_nested_observation_spaces",
main = "tests/test_nested_observation_spaces.py",
tags = ["team:rllib", "tests_dir", "tests_dir_N"],
size = "medium",
srcs = ["tests/test_nested_observation_spaces.py"]
)
py_test(
name = "tests/test_nn_framework_import_errors",
tags = ["team:rllib", "tests_dir", "tests_dir_N"],
size = "small",
srcs = ["tests/test_nn_framework_import_errors.py"]
)
py_test(
name = "tests/test_pettingzoo_env",
tags = ["team:rllib", "tests_dir", "tests_dir_P"],
size = "medium",
srcs = ["tests/test_pettingzoo_env.py"]
)
py_test(
name = "tests/test_placement_groups",
tags = ["team:rllib", "tests_dir", "tests_dir_P"],
size = "medium",
srcs = ["tests/test_placement_groups.py"]
)
py_test(
name = "tests/test_ray_client",
tags = ["team:rllib", "tests_dir", "tests_dir_R"],
size = "large",
srcs = ["tests/test_ray_client.py"]
)
py_test(
name = "tests/test_reproducibility",
tags = ["team:rllib", "tests_dir", "tests_dir_R"],
size = "medium",
srcs = ["tests/test_reproducibility.py"]
)
# Test [train|evaluate].py scripts (w/o confirming evaluation performance).
py_test(
name = "test_rllib_evaluate_1",
main = "tests/test_rllib_train_and_evaluate.py",
tags = ["team:rllib", "tests_dir", "tests_dir_R"],
size = "large",
data = ["train.py", "evaluate.py"],
srcs = ["tests/test_rllib_train_and_evaluate.py"],
args = ["TestEvaluate1"]
)
py_test(
name = "test_rllib_evaluate_2",
main = "tests/test_rllib_train_and_evaluate.py",
tags = ["team:rllib", "tests_dir", "tests_dir_R"],
size = "large",
data = ["train.py", "evaluate.py"],
srcs = ["tests/test_rllib_train_and_evaluate.py"],
args = ["TestEvaluate2"]
)
py_test(
name = "test_rllib_evaluate_3",
main = "tests/test_rllib_train_and_evaluate.py",
tags = ["team:rllib", "tests_dir", "tests_dir_R"],
size = "large",
data = ["train.py", "evaluate.py"],
srcs = ["tests/test_rllib_train_and_evaluate.py"],
args = ["TestEvaluate3"]
)
py_test(
name = "test_rllib_evaluate_4",
main = "tests/test_rllib_train_and_evaluate.py",
tags = ["team:rllib", "tests_dir", "tests_dir_R"],
size = "large",
data = ["train.py", "evaluate.py"],
srcs = ["tests/test_rllib_train_and_evaluate.py"],
args = ["TestEvaluate4"]
)
# Test [train|evaluate].py scripts (and confirm `rllib evaluate` performance is same
# as the final one from the `rllib train` run).
py_test(
name = "test_rllib_train_and_evaluate",
main = "tests/test_rllib_train_and_evaluate.py",
tags = ["team:rllib", "tests_dir", "tests_dir_R"],
size = "large",
data = ["train.py", "evaluate.py"],
srcs = ["tests/test_rllib_train_and_evaluate.py"],
args = ["TestTrainAndEvaluate"]
)
py_test(
name = "tests/test_supported_multi_agent_pg",
main = "tests/test_supported_multi_agent.py",
tags = ["team:rllib", "tests_dir", "tests_dir_S"],
size = "medium",
srcs = ["tests/test_supported_multi_agent.py"],
args = ["TestSupportedMultiAgentPG"]
)
py_test(
name = "tests/test_supported_multi_agent_off_policy",
main = "tests/test_supported_multi_agent.py",
tags = ["team:rllib", "tests_dir", "tests_dir_S"],
size = "medium",
srcs = ["tests/test_supported_multi_agent.py"],
args = ["TestSupportedMultiAgentOffPolicy"]
)
py_test(
name = "tests/test_supported_spaces_pg",
main = "tests/test_supported_spaces.py",
tags = ["team:rllib", "tests_dir", "tests_dir_S"],
size = "large",
srcs = ["tests/test_supported_spaces.py"],
args = ["TestSupportedSpacesPG"]
)
py_test(
name = "tests/test_supported_spaces_off_policy",
main = "tests/test_supported_spaces.py",
tags = ["team:rllib", "tests_dir", "tests_dir_S"],
size = "medium",
srcs = ["tests/test_supported_spaces.py"],
args = ["TestSupportedSpacesOffPolicy"]
)
py_test(
name = "tests/test_supported_spaces_evolution_algos",
main = "tests/test_supported_spaces.py",
tags = ["team:rllib", "tests_dir", "tests_dir_S"],
size = "large",
srcs = ["tests/test_supported_spaces.py"],
args = ["TestSupportedSpacesEvolutionAlgos"]
)
py_test(
name = "tests/test_timesteps",
tags = ["team:rllib", "tests_dir", "tests_dir_T"],
size = "small",
srcs = ["tests/test_timesteps.py"]
)
# --------------------------------------------------------------------
# examples/ directory (excluding examples/documentation/...)
#
# Tag: examples, examples_[A-Z]
#
# NOTE: Add tests alphabetically into this list and make sure, to tag
# it correctly by its starting letter, e.g. tags=["examples", "examples_A"]
# for `examples/all_stuff.py`.
# --------------------------------------------------------------------
py_test(
name = "examples/action_masking_tf",
main = "examples/action_masking.py",
tags = ["team:rllib", "exclusive", "examples", "examples_A"],
size = "medium",
srcs = ["examples/action_masking.py"],
args = ["--stop-iter=2"]
)
py_test(
name = "examples/action_masking_torch",
main = "examples/action_masking.py",
tags = ["team:rllib", "exclusive", "examples", "examples_A"],
size = "medium",
srcs = ["examples/action_masking.py"],
args = ["--stop-iter=2", "--framework=torch"]
)
py_test(
name = "examples/attention_net_tf",
main = "examples/attention_net.py",
tags = ["team:rllib", "exclusive", "examples", "examples_A"],
size = "medium",
srcs = ["examples/attention_net.py"],
args = ["--as-test", "--stop-reward=70"]
)
py_test(
name = "examples/attention_net_torch",
main = "examples/attention_net.py",
tags = ["team:rllib", "exclusive", "examples", "examples_A"],
size = "medium",
srcs = ["examples/attention_net.py"],
args = ["--as-test", "--stop-reward=70", "--framework torch"]
)
py_test(
name = "examples/autoregressive_action_dist_tf",
main = "examples/autoregressive_action_dist.py",
tags = ["team:rllib", "exclusive", "examples", "examples_A"],
size = "medium",
srcs = ["examples/autoregressive_action_dist.py"],
args = ["--as-test", "--stop-reward=150", "--num-cpus=4"]
)
py_test(
name = "examples/autoregressive_action_dist_torch",
main = "examples/autoregressive_action_dist.py",
tags = ["team:rllib", "exclusive", "examples", "examples_A"],
size = "medium",
srcs = ["examples/autoregressive_action_dist.py"],
args = ["--as-test", "--framework=torch", "--stop-reward=150", "--num-cpus=4"]
)
py_test(
name = "examples/bare_metal_policy_with_custom_view_reqs",
main = "examples/bare_metal_policy_with_custom_view_reqs.py",
tags = ["team:rllib", "exclusive", "examples", "examples_B"],
size = "medium",
srcs = ["examples/bare_metal_policy_with_custom_view_reqs.py"],
)
py_test(
name = "examples/batch_norm_model_ppo_tf",
main = "examples/batch_norm_model.py",
tags = ["team:rllib", "exclusive", "examples", "examples_B"],
size = "medium",
srcs = ["examples/batch_norm_model.py"],
args = ["--as-test", "--run=PPO", "--stop-reward=80"]
)
py_test(
name = "examples/batch_norm_model_ppo_torch",
main = "examples/batch_norm_model.py",
tags = ["team:rllib", "exclusive", "examples", "examples_B"],
size = "medium",
srcs = ["examples/batch_norm_model.py"],
args = ["--as-test", "--framework=torch", "--run=PPO", "--stop-reward=80"]
)
py_test(
name = "examples/batch_norm_model_dqn_tf",
main = "examples/batch_norm_model.py",
tags = ["team:rllib", "exclusive", "examples", "examples_B"],
size = "medium",
srcs = ["examples/batch_norm_model.py"],
args = ["--as-test", "--run=DQN", "--stop-reward=70"]
)
py_test(
name = "examples/batch_norm_model_dqn_torch",
main = "examples/batch_norm_model.py",
tags = ["team:rllib", "exclusive", "examples", "examples_B"],
size = "large", # DQN learns much slower with BatchNorm.
srcs = ["examples/batch_norm_model.py"],
args = ["--as-test", "--framework=torch", "--run=DQN", "--stop-reward=70"]
)
py_test(
name = "examples/batch_norm_model_ddpg_tf",
main = "examples/batch_norm_model.py",
tags = ["team:rllib", "exclusive", "examples", "examples_B"],
size = "medium",
srcs = ["examples/batch_norm_model.py"],
args = ["--run=DDPG", "--stop-iters=1"]
)
py_test(
name = "examples/batch_norm_model_ddpg_torch",
main = "examples/batch_norm_model.py",
tags = ["team:rllib", "exclusive", "examples", "examples_B"],
size = "medium",
srcs = ["examples/batch_norm_model.py"],
args = ["--framework=torch", "--run=DDPG", "--stop-iters=1"]
)
py_test(
name = "examples/cartpole_lstm_impala_tf",
main = "examples/cartpole_lstm.py",
tags = ["team:rllib", "exclusive", "examples", "examples_C", "examples_C_AtoT"],
size = "medium",
srcs = ["examples/cartpole_lstm.py"],
args = ["--as-test", "--run=IMPALA", "--stop-reward=40", "--num-cpus=4"]
)
py_test(
name = "examples/cartpole_lstm_impala_torch",
main = "examples/cartpole_lstm.py",
tags = ["team:rllib", "exclusive", "examples", "examples_C", "examples_C_AtoT"],
size = "medium",
srcs = ["examples/cartpole_lstm.py"],
args = ["--as-test", "--framework=torch", "--run=IMPALA", "--stop-reward=40", "--num-cpus=4"]
)
py_test(
name = "examples/cartpole_lstm_ppo_tf",
main = "examples/cartpole_lstm.py",
tags = ["team:rllib", "exclusive", "examples", "examples_C", "examples_C_AtoT"],
size = "medium",
srcs = ["examples/cartpole_lstm.py"],
args = ["--as-test", "--framework=tf", "--run=PPO", "--stop-reward=40", "--num-cpus=4"]
)
py_test(
name = "examples/cartpole_lstm_ppo_tf2",
main = "examples/cartpole_lstm.py",
tags = ["team:rllib", "exclusive", "examples", "examples_C", "examples_C_AtoT"],
size = "large",
srcs = ["examples/cartpole_lstm.py"],
args = ["--as-test", "--framework=tf2", "--run=PPO", "--stop-reward=40", "--num-cpus=4"]
)
py_test(
name = "examples/cartpole_lstm_ppo_torch",
main = "examples/cartpole_lstm.py",
tags = ["team:rllib", "exclusive", "examples", "examples_C", "examples_C_AtoT"],
size = "medium",
srcs = ["examples/cartpole_lstm.py"],
args = ["--as-test", "--framework=torch", "--run=PPO", "--stop-reward=40", "--num-cpus=4"]
)
py_test(
name = "examples/cartpole_lstm_ppo_tf_with_prev_a_and_r",
main = "examples/cartpole_lstm.py",
tags = ["team:rllib", "exclusive", "examples", "examples_C", "examples_C_AtoT"],
size = "medium",
srcs = ["examples/cartpole_lstm.py"],
args = ["--as-test", "--run=PPO", "--stop-reward=40", "--use-prev-action", "--use-prev-reward", "--num-cpus=4"]
)
py_test(
name = "examples/centralized_critic_tf",
main = "examples/centralized_critic.py",
tags = ["team:rllib", "exclusive", "examples", "examples_C", "examples_C_AtoT"],
size = "large",
srcs = ["examples/centralized_critic.py"],
args = ["--as-test", "--stop-reward=7.2"]
)
py_test(
name = "examples/centralized_critic_torch",
main = "examples/centralized_critic.py",
tags = ["team:rllib", "exclusive", "examples", "examples_C", "examples_C_AtoT"],
size = "large",
srcs = ["examples/centralized_critic.py"],
args = ["--as-test", "--framework=torch", "--stop-reward=7.2"]
)
py_test(
name = "examples/centralized_critic_2_tf",
main = "examples/centralized_critic_2.py",
tags = ["team:rllib", "exclusive", "examples", "examples_C", "examples_C_AtoT"],
size = "medium",
srcs = ["examples/centralized_critic_2.py"],
args = ["--as-test", "--stop-reward=6.0"]
)
py_test(
name = "examples/centralized_critic_2_torch",
main = "examples/centralized_critic_2.py",
tags = ["team:rllib", "exclusive", "examples", "examples_C", "examples_C_AtoT"],
size = "medium",
srcs = ["examples/centralized_critic_2.py"],
args = ["--as-test", "--framework=torch", "--stop-reward=6.0"]
)
py_test(
name = "examples/checkpoint_by_custom_criteria",
main = "examples/checkpoint_by_custom_criteria.py",
tags = ["team:rllib", "exclusive", "examples", "examples_C", "examples_C_AtoT"],
size = "medium",
srcs = ["examples/checkpoint_by_custom_criteria.py"],
args = ["--stop-iters=3 --num-cpus=3"]
)
py_test(
name = "examples/complex_struct_space_tf",
main = "examples/complex_struct_space.py",
tags = ["team:rllib", "exclusive", "examples", "examples_C", "examples_C_AtoT"],
size = "medium",
srcs = ["examples/complex_struct_space.py"],
args = ["--framework=tf"],
)
py_test(
name = "examples/complex_struct_space_tf_eager",
main = "examples/complex_struct_space.py",
tags = ["team:rllib", "exclusive", "examples", "examples_C", "examples_C_AtoT"],
size = "medium",
srcs = ["examples/complex_struct_space.py"],
args = ["--framework=tfe"],
)
py_test(
name = "examples/complex_struct_space_torch",
main = "examples/complex_struct_space.py",
tags = ["team:rllib", "exclusive", "examples", "examples_C", "examples_C_AtoT"],
size = "medium",
srcs = ["examples/complex_struct_space.py"],
args = ["--framework=torch"],
)
py_test(
name = "examples/curriculum_learning",
main = "examples/curriculum_learning.py",
tags = ["team:rllib", "exclusive", "examples", "examples_C", "examples_C_UtoZ"],
size = "medium",
srcs = ["examples/curriculum_learning.py"],
args = ["--as-test", "--stop-reward=800.0"]
)
py_test(
name = "examples/custom_env_tf",
main = "examples/custom_env.py",
tags = ["team:rllib", "exclusive", "examples", "examples_C", "examples_C_UtoZ"],
size = "medium",
srcs = ["examples/custom_env.py"],
args = ["--as-test"]
)
py_test(
name = "examples/custom_env_torch",
main = "examples/custom_env.py",
tags = ["team:rllib", "exclusive", "examples", "examples_C", "examples_C_UtoZ"],
size = "large",
srcs = ["examples/custom_env.py"],
args = ["--as-test", "--framework=torch"]
)
py_test(
name = "examples/custom_eval_tf",
main = "examples/custom_eval.py",
tags = ["team:rllib", "exclusive", "examples", "examples_C", "examples_C_UtoZ"],
size = "medium",
srcs = ["examples/custom_eval.py"],
args = ["--num-cpus=4", "--as-test"]
)
py_test(
name = "examples/custom_eval_torch",
main = "examples/custom_eval.py",
tags = ["team:rllib", "exclusive", "examples", "examples_C", "examples_C_UtoZ"],
size = "medium",
srcs = ["examples/custom_eval.py"],
args = ["--num-cpus=4", "--as-test", "--framework=torch"]
)
py_test(
name = "examples/custom_experiment",
main = "examples/custom_experiment.py",
tags = ["team:rllib", "exclusive", "examples", "examples_C", "examples_C_UtoZ"],
size = "medium",
srcs = ["examples/custom_experiment.py"],
args = ["--train-iterations=10"]
)
py_test(
name = "examples/custom_fast_model_tf",
main = "examples/custom_fast_model.py",
tags = ["team:rllib", "exclusive", "examples", "examples_C", "examples_C_UtoZ"],
size = "medium",
srcs = ["examples/custom_fast_model.py"],
args = ["--stop-iters=1"]
)
py_test(
name = "examples/custom_fast_model_torch",
main = "examples/custom_fast_model.py",
tags = ["team:rllib", "exclusive", "examples", "examples_C", "examples_C_UtoZ"],
size = "medium",
srcs = ["examples/custom_fast_model.py"],
args = ["--stop-iters=1", "--framework=torch"]
)
py_test(
name = "examples/custom_keras_model_a2c",
main = "examples/custom_keras_model.py",
tags = ["team:rllib", "exclusive", "examples", "examples_C", "examples_C_UtoZ"],
size = "large",
srcs = ["examples/custom_keras_model.py"],
args = ["--run=A2C", "--stop=50", "--num-cpus=4"]
)
py_test(
name = "examples/custom_keras_model_dqn",
main = "examples/custom_keras_model.py",
tags = ["team:rllib", "exclusive", "examples", "examples_C", "examples_C_UtoZ"],
size = "medium",
srcs = ["examples/custom_keras_model.py"],
args = ["--run=DQN", "--stop=50"]
)
py_test(
name = "examples/custom_keras_model_ppo",
main = "examples/custom_keras_model.py",
tags = ["team:rllib", "exclusive", "examples", "examples_C", "examples_C_UtoZ"],
size = "medium",
srcs = ["examples/custom_keras_model.py"],
args = ["--run=PPO", "--stop=50", "--num-cpus=4"]
)
py_test(
name = "examples/custom_metrics_and_callbacks",
main = "examples/custom_metrics_and_callbacks.py",
tags = ["team:rllib", "exclusive", "examples", "examples_C", "examples_C_UtoZ"],
size = "small",
srcs = ["examples/custom_metrics_and_callbacks.py"],
args = ["--stop-iters=2"]
)
py_test(
name = "examples/custom_metrics_and_callbacks_legacy",
main = "examples/custom_metrics_and_callbacks_legacy.py",
tags = ["team:rllib", "exclusive", "examples", "examples_C", "examples_C_UtoZ"],
size = "small",
srcs = ["examples/custom_metrics_and_callbacks_legacy.py"],
args = ["--stop-iters=2"]
)
py_test(
name = "examples/custom_model_api_tf",
main = "examples/custom_model_api.py",
tags = ["team:rllib", "exclusive", "examples", "examples_C", "examples_C_UtoZ"],
size = "small",
srcs = ["examples/custom_model_api.py"],
)
py_test(
name = "examples/custom_model_api_torch",
main = "examples/custom_model_api.py",
tags = ["team:rllib", "exclusive", "examples", "examples_C", "examples_C_UtoZ"],
size = "small",
srcs = ["examples/custom_model_api.py"],
args = ["--framework=torch"],
)
py_test(
name = "examples/custom_model_loss_and_metrics_ppo_tf",
main = "examples/custom_model_loss_and_metrics.py",
tags = ["team:rllib", "exclusive", "examples", "examples_C", "examples_C_UtoZ"],
size = "medium",
# Include the json data file.
data = ["tests/data/cartpole/small.json"],
srcs = ["examples/custom_model_loss_and_metrics.py"],
args = ["--run=PPO", "--stop-iters=1", "--input-files=tests/data/cartpole"]
)
py_test(
name = "examples/custom_model_loss_and_metrics_ppo_torch",
main = "examples/custom_model_loss_and_metrics.py",
tags = ["team:rllib", "exclusive", "examples", "examples_C", "examples_C_UtoZ"],
size = "medium",
# Include the json data file.
data = ["tests/data/cartpole/small.json"],
srcs = ["examples/custom_model_loss_and_metrics.py"],
args = ["--run=PPO", "--framework=torch", "--stop-iters=1", "--input-files=tests/data/cartpole"]
)
py_test(
name = "examples/custom_model_loss_and_metrics_pg_tf",
main = "examples/custom_model_loss_and_metrics.py",
tags = ["team:rllib", "exclusive", "examples", "examples_C", "examples_C_UtoZ"],
size = "medium",
# Include the json data file.
data = ["tests/data/cartpole/small.json"],
srcs = ["examples/custom_model_loss_and_metrics.py"],
args = ["--run=PG", "--stop-iters=1", "--input-files=tests/data/cartpole"]
)
py_test(
name = "examples/custom_model_loss_and_metrics_pg_torch",
main = "examples/custom_model_loss_and_metrics.py",
tags = ["team:rllib", "exclusive", "examples", "examples_C", "examples_C_UtoZ"],
size = "medium",
# Include the json data file.
data = ["tests/data/cartpole/small.json"],
srcs = ["examples/custom_model_loss_and_metrics.py"],
args = ["--run=PG", "--framework=torch", "--stop-iters=1", "--input-files=tests/data/cartpole"]
)
py_test(
name = "examples/custom_observation_filters",
main = "examples/custom_observation_filters.py",
tags = ["team:rllib", "exclusive", "examples", "examples_C", "examples_C_UtoZ"],
size = "medium",
srcs = ["examples/custom_observation_filters.py"],
args = ["--stop-iters=3"]
)
py_test(
name = "examples/custom_rnn_model_repeat_after_me_tf",
main = "examples/custom_rnn_model.py",
tags = ["team:rllib", "exclusive", "examples", "examples_C", "examples_C_UtoZ"],
size = "medium",
srcs = ["examples/custom_rnn_model.py"],
args = ["--as-test", "--run=PPO", "--stop-reward=40", "--env=RepeatAfterMeEnv", "--num-cpus=4"]
)
py_test(
name = "examples/custom_rnn_model_repeat_initial_obs_tf",
main = "examples/custom_rnn_model.py",
tags = ["team:rllib", "exclusive", "examples", "examples_C", "examples_C_UtoZ"],
size = "medium",
srcs = ["examples/custom_rnn_model.py"],
args = ["--as-test", "--run=PPO", "--stop-reward=10", "--stop-timesteps=300000", "--env=RepeatInitialObsEnv", "--num-cpus=4"]
)
py_test(
name = "examples/custom_rnn_model_repeat_after_me_torch",
main = "examples/custom_rnn_model.py",
tags = ["team:rllib", "exclusive", "examples", "examples_C", "examples_C_UtoZ"],
size = "medium",
srcs = ["examples/custom_rnn_model.py"],
args = ["--as-test", "--framework=torch", "--run=PPO", "--stop-reward=40", "--env=RepeatAfterMeEnv", "--num-cpus=4"]
)
py_test(
name = "examples/custom_rnn_model_repeat_initial_obs_torch",
main = "examples/custom_rnn_model.py",
tags = ["team:rllib", "exclusive", "examples", "examples_C", "examples_C_UtoZ"],
size = "medium",
srcs = ["examples/custom_rnn_model.py"],
args = ["--as-test", "--framework=torch", "--run=PPO", "--stop-reward=10", "--stop-timesteps=300000", "--env=RepeatInitialObsEnv", "--num-cpus=4"]
)
py_test(
name = "examples/custom_tf_policy",
tags = ["team:rllib", "exclusive", "examples", "examples_C", "examples_C_UtoZ"],
size = "medium",
srcs = ["examples/custom_tf_policy.py"],
args = ["--stop-iters=2", "--num-cpus=4"]
)
py_test(
name = "examples/custom_torch_policy",
tags = ["team:rllib", "exclusive", "examples", "examples_C", "examples_C_UtoZ"],
size = "medium",
srcs = ["examples/custom_torch_policy.py"],
args = ["--stop-iters=2", "--num-cpus=4"]
)
py_test(
name = "examples/custom_train_fn",
main = "examples/custom_train_fn.py",
tags = ["team:rllib", "exclusive", "examples", "examples_C", "examples_C_UtoZ"],
size = "medium",
srcs = ["examples/custom_train_fn.py"],
)
py_test(
name = "examples/custom_vector_env_tf",
main = "examples/custom_vector_env.py",
tags = ["team:rllib", "exclusive", "examples", "examples_C", "examples_C_UtoZ"],
size = "medium",
srcs = ["examples/custom_vector_env.py"],
args = ["--as-test", "--stop-reward=40.0"]
)
py_test(
name = "examples/custom_vector_env_torch",
main = "examples/custom_vector_env.py",
tags = ["team:rllib", "exclusive", "examples", "examples_C", "examples_C_UtoZ"],
size = "medium",
srcs = ["examples/custom_vector_env.py"],
args = ["--as-test", "--framework=torch", "--stop-reward=40.0"]
)
py_test(
name = "examples/deterministic_training_tf",
main = "examples/deterministic_training.py",
tags = ["team:rllib", "exclusive", "multi_gpu", "examples"],
size = "medium",
srcs = ["examples/deterministic_training.py"],
args = ["--as-test", "--stop-iters=1", "--framework=tf", "--num-gpus=1", "--num-gpus-per-worker=1"]
)
py_test(
name = "examples/deterministic_training_tf2",
main = "examples/deterministic_training.py",
tags = ["team:rllib", "exclusive", "multi_gpu", "examples"],
size = "medium",
srcs = ["examples/deterministic_training.py"],
args = ["--as-test", "--stop-iters=1", "--framework=tf2", "--num-gpus=1", "--num-gpus-per-worker=1"]
)
py_test(
name = "examples/deterministic_training_torch",
main = "examples/deterministic_training.py",
tags = ["team:rllib", "exclusive", "multi_gpu", "examples"],
size = "medium",
srcs = ["examples/deterministic_training.py"],
args = ["--as-test", "--stop-iters=1", "--framework=torch", "--num-gpus=1", "--num-gpus-per-worker=1"]
)
py_test(
name = "examples/eager_execution",
tags = ["team:rllib", "exclusive", "examples", "examples_E"],
size = "small",
srcs = ["examples/eager_execution.py"],
args = ["--stop-iters=2"]
)
py_test(
name = "examples/export/cartpole_dqn_export",
main = "examples/export/cartpole_dqn_export.py",
tags = ["team:rllib", "exclusive", "examples", "examples_E"],
size = "medium",
srcs = ["examples/export/cartpole_dqn_export.py"],
)
py_test(
name = "examples/export/onnx_tf",
main = "examples/export/onnx_tf.py",
tags = ["team:rllib", "exclusive", "examples", "examples_E", "no_main"],
size = "medium",
srcs = ["examples/export/onnx_tf.py"],
)
py_test(
name = "examples/export/onnx_torch",
main = "examples/export/onnx_torch.py",
tags = ["team:rllib", "exclusive", "examples", "examples_E", "no_main"],
size = "medium",
srcs = ["examples/export/onnx_torch.py"],
)
py_test(
name = "examples/fractional_gpus",
main = "examples/fractional_gpus.py",
tags = ["team:rllib", "exclusive", "examples", "examples_F"],
size = "medium",
srcs = ["examples/fractional_gpus.py"],
args = ["--as-test", "--stop-reward=40.0", "--num-gpus=0", "--num-workers=0"]
)
py_test(
name = "examples/hierarchical_training_tf",
main = "examples/hierarchical_training.py",
tags = ["team:rllib", "exclusive", "examples", "examples_H"],
size = "medium",
srcs = ["examples/hierarchical_training.py"],
args = ["--stop-reward=0.0"]
)
py_test(
name = "examples/hierarchical_training_torch",
main = "examples/hierarchical_training.py",
tags = ["team:rllib", "exclusive", "examples", "examples_H"],
size = "medium",
srcs = ["examples/hierarchical_training.py"],
args = ["--framework=torch", "--stop-reward=0.0"]
)
# Do not run this test (MobileNetV2 is gigantic and takes forever for 1 iter).
# py_test(
# name = "examples/mobilenet_v2_with_lstm_tf",
# main = "examples/mobilenet_v2_with_lstm.py",
# tags = ["team:rllib", "examples", "examples_M"],
# size = "small",
# srcs = ["examples/mobilenet_v2_with_lstm.py"]
# )
py_test(
name = "examples/multi_agent_cartpole_tf",
main = "examples/multi_agent_cartpole.py",
tags = ["team:rllib", "exclusive", "examples", "examples_M"],
size = "medium",
srcs = ["examples/multi_agent_cartpole.py"],
args = ["--as-test", "--stop-reward=70.0", "--num-cpus=4"]
)
py_test(
name = "examples/multi_agent_cartpole_torch",
main = "examples/multi_agent_cartpole.py",
tags = ["team:rllib", "exclusive", "examples", "examples_M"],
size = "medium",
srcs = ["examples/multi_agent_cartpole.py"],
args = ["--as-test", "--framework=torch", "--stop-reward=70.0", "--num-cpus=4"]
)
py_test(
name = "examples/multi_agent_custom_policy_tf",
main = "examples/multi_agent_custom_policy.py",
tags = ["team:rllib", "exclusive", "examples", "examples_M"],
size = "small",
srcs = ["examples/multi_agent_custom_policy.py"],
args = ["--as-test", "--stop-reward=80"]
)
py_test(
name = "examples/multi_agent_custom_policy_torch",
main = "examples/multi_agent_custom_policy.py",
tags = ["team:rllib", "exclusive", "examples", "examples_M"],
size = "small",
srcs = ["examples/multi_agent_custom_policy.py"],
args = ["--as-test", "--framework=torch", "--stop-reward=80"]
)
py_test(
name = "examples/multi_agent_different_spaces_for_agents_tf2",
main = "examples/multi_agent_different_spaces_for_agents.py",
tags = ["team:rllib", "exclusive", "examples", "examples_M"],
size = "medium",
srcs = ["examples/multi_agent_different_spaces_for_agents.py"],
args = ["--stop-iters=4", "--framework=tf2", "--eager-tracing"]
)
py_test(
name = "examples/multi_agent_different_spaces_for_agents_torch",
main = "examples/multi_agent_different_spaces_for_agents.py",
tags = ["team:rllib", "exclusive", "examples", "examples_M"],
size = "medium",
srcs = ["examples/multi_agent_different_spaces_for_agents.py"],
args = ["--stop-iters=4", "--framework=torch"]
)
py_test(
name = "examples/multi_agent_two_trainers_tf",
main = "examples/multi_agent_two_trainers.py",
tags = ["team:rllib", "exclusive", "examples", "examples_M"],
size = "medium",
srcs = ["examples/multi_agent_two_trainers.py"],
args = ["--as-test", "--stop-reward=70"]
)
py_test(
name = "examples/multi_agent_two_trainers_torch",
main = "examples/multi_agent_two_trainers.py",
tags = ["team:rllib", "exclusive", "examples", "examples_M"],
size = "medium",
srcs = ["examples/multi_agent_two_trainers.py"],
args = ["--as-test", "--framework=torch", "--stop-reward=70"]
)
# Taking out this test for now: Mixed torch- and tf- policies within the same
# Trainer never really worked.
# py_test(
# name = "examples/multi_agent_two_trainers_mixed_torch_tf",
# main = "examples/multi_agent_two_trainers.py",
# tags = ["team:rllib", "exclusive", "examples", "examples_M"],
# size = "medium",
# srcs = ["examples/multi_agent_two_trainers.py"],
# args = ["--as-test", "--mixed-torch-tf", "--stop-reward=70"]
# )
py_test(
name = "examples/nested_action_spaces_ppo_tf",
main = "examples/nested_action_spaces.py",
tags = ["team:rllib", "exclusive", "examples", "examples_N"],
size = "medium",
srcs = ["examples/nested_action_spaces.py"],
args = ["--as-test", "--stop-reward=-600", "--run=PPO"]
)
py_test(
name = "examples/nested_action_spaces_ppo_torch",
main = "examples/nested_action_spaces.py",
tags = ["team:rllib", "exclusive", "examples", "examples_N"],
size = "medium",
srcs = ["examples/nested_action_spaces.py"],
args = ["--as-test", "--framework=torch", "--stop-reward=-600", "--run=PPO"]
)
py_test(
name = "examples/parallel_evaluation_and_training_13_episodes_tf",
main = "examples/parallel_evaluation_and_training.py",
tags = ["team:rllib", "exclusive", "examples", "examples_P"],
size = "medium",
srcs = ["examples/parallel_evaluation_and_training.py"],
args = ["--as-test", "--stop-reward=50.0", "--num-cpus=6", "--evaluation-duration=13"]
)
py_test(
name = "examples/parallel_evaluation_and_training_auto_episodes_tf",
main = "examples/parallel_evaluation_and_training.py",
tags = ["team:rllib", "exclusive", "examples", "examples_P"],
size = "medium",
srcs = ["examples/parallel_evaluation_and_training.py"],
args = ["--as-test", "--stop-reward=50.0", "--num-cpus=6", "--evaluation-duration=auto"]
)
py_test(
name = "examples/parallel_evaluation_and_training_211_ts_tf2",
main = "examples/parallel_evaluation_and_training.py",
tags = ["team:rllib", "exclusive", "examples", "examples_P"],
size = "medium",
srcs = ["examples/parallel_evaluation_and_training.py"],
args = ["--as-test", "--framework=tf2", "--stop-reward=30.0", "--num-cpus=6", "--evaluation-num-workers=3", "--evaluation-duration=211", "--evaluation-duration-unit=timesteps"]
)
py_test(
name = "examples/parallel_evaluation_and_training_auto_ts_torch",
main = "examples/parallel_evaluation_and_training.py",
tags = ["team:rllib", "exclusive", "examples", "examples_P"],
size = "medium",
srcs = ["examples/parallel_evaluation_and_training.py"],
args = ["--as-test", "--framework=torch", "--stop-reward=30.0", "--num-cpus=6", "--evaluation-num-workers=3", "--evaluation-duration=auto", "--evaluation-duration-unit=timesteps"]
)
py_test(
name = "examples/parametric_actions_cartpole_pg_tf",
main = "examples/parametric_actions_cartpole.py",
tags = ["team:rllib", "exclusive", "examples", "examples_P"],
size = "medium",
srcs = ["examples/parametric_actions_cartpole.py"],
args = ["--as-test", "--stop-reward=60.0", "--run=PG"]
)
py_test(
name = "examples/parametric_actions_cartpole_dqn_tf",
main = "examples/parametric_actions_cartpole.py",
tags = ["team:rllib", "exclusive", "examples", "examples_P"],
size = "medium",
srcs = ["examples/parametric_actions_cartpole.py"],
args = ["--as-test", "--stop-reward=60.0", "--run=DQN"]
)
py_test(
name = "examples/parametric_actions_cartpole_pg_torch",
main = "examples/parametric_actions_cartpole.py",
tags = ["team:rllib", "exclusive", "examples", "examples_P"],
size = "medium",
srcs = ["examples/parametric_actions_cartpole.py"],
args = ["--as-test", "--framework=torch", "--stop-reward=60.0", "--run=PG"]
)
py_test(
name = "examples/parametric_actions_cartpole_dqn_torch",
main = "examples/parametric_actions_cartpole.py",
tags = ["team:rllib", "exclusive", "examples", "examples_P"],
size = "medium",
srcs = ["examples/parametric_actions_cartpole.py"],
args = ["--as-test", "--framework=torch", "--stop-reward=60.0", "--run=DQN"]
)
py_test(
name = "examples/parametric_actions_cartpole_embeddings_learnt_by_model",
main = "examples/parametric_actions_cartpole_embeddings_learnt_by_model.py",
tags = ["team:rllib", "exclusive", "examples", "examples_P"],
size = "medium",
srcs = ["examples/parametric_actions_cartpole_embeddings_learnt_by_model.py"],
args = ["--as-test", "--stop-reward=80.0"]
)
py_test(
name = "examples/inference_and_serving/policy_inference_after_training_tf",
main = "examples/inference_and_serving/policy_inference_after_training.py",
tags = ["team:rllib", "exclusive", "examples", "examples_P"],
size = "medium",
srcs = ["examples/inference_and_serving/policy_inference_after_training.py"],
args = ["--stop-iters=3", "--framework=tf"]
)
py_test(
name = "examples/inference_and_serving/policy_inference_after_training_torch",
main = "examples/inference_and_serving/policy_inference_after_training.py",
tags = ["team:rllib", "exclusive", "examples", "examples_P"],
size = "medium",
srcs = ["examples/inference_and_serving/policy_inference_after_training.py"],
args = ["--stop-iters=3", "--framework=torch"]
)
py_test(
name = "examples/inference_and_serving/policy_inference_after_training_with_attention_tf",
main = "examples/inference_and_serving/policy_inference_after_training_with_attention.py",
tags = ["team:rllib", "exclusive", "examples", "examples_P"],
size = "medium",
srcs = ["examples/inference_and_serving/policy_inference_after_training_with_attention.py"],
args = ["--stop-iters=2", "--framework=tf"]
)
py_test(
name = "examples/inference_and_serving/policy_inference_after_training_with_attention_torch",
main = "examples/inference_and_serving/policy_inference_after_training_with_attention.py",
tags = ["team:rllib", "exclusive", "examples", "examples_P"],
size = "medium",
srcs = ["examples/inference_and_serving/policy_inference_after_training_with_attention.py"],
args = ["--stop-iters=2", "--framework=torch"]
)
py_test(
name = "examples/inference_and_serving/policy_inference_after_training_with_lstm_tf",
main = "examples/inference_and_serving/policy_inference_after_training_with_lstm.py",
tags = ["team:rllib", "exclusive", "examples", "examples_P"],
size = "medium",
srcs = ["examples/inference_and_serving/policy_inference_after_training_with_lstm.py"],
args = ["--stop-iters=1", "--framework=tf"]
)
py_test(
name = "examples/inference_and_serving/policy_inference_after_training_with_lstm_torch",
main = "examples/inference_and_serving/policy_inference_after_training_with_lstm.py",
tags = ["team:rllib", "exclusive", "examples", "examples_P"],
size = "medium",
srcs = ["examples/inference_and_serving/policy_inference_after_training_with_lstm.py"],
args = ["--stop-iters=1", "--framework=torch"]
)
py_test(
name = "examples/preprocessing_disabled_tf",
main = "examples/preprocessing_disabled.py",
tags = ["team:rllib", "exclusive", "examples", "examples_P"],
size = "medium",
srcs = ["examples/preprocessing_disabled.py"],
args = ["--stop-iters=2"]
)
py_test(
name = "examples/preprocessing_disabled_torch",
main = "examples/preprocessing_disabled.py",
tags = ["team:rllib", "exclusive", "examples", "examples_P"],
size = "medium",
srcs = ["examples/preprocessing_disabled.py"],
args = ["--framework=torch", "--stop-iters=2"]
)
py_test(
name = "examples/recommender_system_with_recsim_and_slateq_tf2",
main = "examples/recommender_system_with_recsim_and_slateq.py",
tags = ["team:rllib", "exclusive", "examples", "examples_R"],
size = "large",
srcs = ["examples/recommender_system_with_recsim_and_slateq.py"],
args = ["--stop-iters=2", "--learning-starts=100", "--framework=tf2", "--use-tune", "--random-test-episodes=10", "--env-num-candidates=50", "--env-slate-size=2"],
)
py_test(
name = "examples/remote_envs_with_inference_done_on_main_node_tf",
main = "examples/remote_envs_with_inference_done_on_main_node.py",
tags = ["team:rllib", "exclusive", "examples", "examples_R"],
size = "medium",
srcs = ["examples/remote_envs_with_inference_done_on_main_node.py"],
args = ["--as-test"],
)
py_test(
name = "examples/remote_envs_with_inference_done_on_main_node_torch",
main = "examples/remote_envs_with_inference_done_on_main_node.py",
tags = ["team:rllib", "exclusive", "examples", "examples_R"],
size = "medium",
srcs = ["examples/remote_envs_with_inference_done_on_main_node.py"],
args = ["--as-test", "--framework=torch"],
)
2022-05-23 08:18:44 +02:00
# py_test(
# name = "examples/remote_base_env_with_custom_api",
# tags = ["team:rllib", "exclusive", "examples", "examples_R"],
2022-05-23 08:18:44 +02:00
# size = "medium",
# srcs = ["examples/remote_base_env_with_custom_api.py"],
# args = ["--stop-iters=3"]
# )
py_test(
name = "examples/replay_buffer_api",
tags = ["team:rllib", "gpu"],
size = "large",
srcs = ["examples/replay_buffer_api.py"],
args = ["--as-test", "--stop-reward=70"]
)
py_test(
name = "examples/restore_1_of_n_agents_from_checkpoint",
tags = ["team:rllib", "exclusive", "examples", "examples_R"],
size = "medium",
srcs = ["examples/restore_1_of_n_agents_from_checkpoint.py"],
args = ["--pre-training-iters=1", "--stop-iters=1", "--num-cpus=4"]
)
py_test(
name = "examples/rnnsac_stateless_cartpole",
tags = ["team:rllib", "exclusive", "gpu"],
size = "large",
srcs = ["examples/rnnsac_stateless_cartpole.py"]
)
py_test(
name = "examples/rollout_worker_custom_workflow",
tags = ["team:rllib", "exclusive", "examples", "examples_R"],
size = "medium",
srcs = ["examples/rollout_worker_custom_workflow.py"],
args = ["--num-cpus=4"]
)
py_test(
name = "examples/rock_paper_scissors_multiagent_tf",
main = "examples/rock_paper_scissors_multiagent.py",
tags = ["team:rllib", "exclusive", "examples", "examples_R"],
size = "medium",
srcs = ["examples/rock_paper_scissors_multiagent.py"],
args = ["--as-test"],
)
py_test(
name = "examples/rock_paper_scissors_multiagent_torch",
main = "examples/rock_paper_scissors_multiagent.py",
tags = ["team:rllib", "exclusive", "examples", "examples_R"],
size = "medium",
srcs = ["examples/rock_paper_scissors_multiagent.py"],
args = ["--as-test", "--framework=torch"],
)
py_test(
name = "examples/self_play_with_open_spiel_connect_4_tf",
main = "examples/self_play_with_open_spiel.py",
tags = ["team:rllib", "exclusive", "examples", "examples_S"],
size = "medium",
srcs = ["examples/self_play_with_open_spiel.py"],
args = ["--framework=tf", "--env=connect_four", "--win-rate-threshold=0.6", "--stop-iters=2", "--num-episodes-human-play=0"]
)
py_test(
name = "examples/self_play_with_open_spiel_connect_4_torch",
main = "examples/self_play_with_open_spiel.py",
tags = ["team:rllib", "exclusive", "examples", "examples_S"],
size = "medium",
srcs = ["examples/self_play_with_open_spiel.py"],
args = ["--framework=torch", "--env=connect_four", "--win-rate-threshold=0.6", "--stop-iters=2", "--num-episodes-human-play=0"]
)
py_test(
name = "examples/self_play_league_based_with_open_spiel_markov_soccer_tf",
main = "examples/self_play_league_based_with_open_spiel.py",
tags = ["team:rllib", "exclusive", "examples", "examples_S"],
size = "medium",
srcs = ["examples/self_play_league_based_with_open_spiel.py"],
args = ["--framework=tf", "--env=markov_soccer", "--win-rate-threshold=0.6", "--stop-iters=2", "--num-episodes-human-play=0"]
)
py_test(
name = "examples/self_play_league_based_with_open_spiel_markov_soccer_torch",
main = "examples/self_play_league_based_with_open_spiel.py",
tags = ["team:rllib", "exclusive", "examples", "examples_S"],
size = "medium",
srcs = ["examples/self_play_league_based_with_open_spiel.py"],
args = ["--framework=torch", "--env=markov_soccer", "--win-rate-threshold=0.6", "--stop-iters=2", "--num-episodes-human-play=0"]
)
py_test(
name = "examples/trajectory_view_api_tf",
main = "examples/trajectory_view_api.py",
tags = ["team:rllib", "exclusive", "examples", "examples_T"],
size = "medium",
srcs = ["examples/trajectory_view_api.py"],
args = ["--as-test", "--framework=tf", "--stop-reward=100.0"]
)
py_test(
name = "examples/trajectory_view_api_torch",
main = "examples/trajectory_view_api.py",
tags = ["team:rllib", "exclusive", "examples", "examples_T"],
size = "medium",
srcs = ["examples/trajectory_view_api.py"],
args = ["--as-test", "--framework=torch", "--stop-reward=100.0"]
)
[RLlib] Add an RLlib Tune experiment to UserTest suite. (#19807) * Add an RLlib Tune experiment to UserTest suite. * Add ray.init() * Move example script to example/tune/, so it can be imported as module. * add __init__.py so our new module will get included in python wheel. * Add block device to RLlib test instances. * Reduce disk size a little bit. * Add metrics reporting * Allow max of 5 workers to accomodate all the worker tasks. * revert disk size change. * Minor updates * Trigger build * set max num workers * Add a compute cfg for autoscaled cpu and gpu nodes. * use 1gpu instance. * install tblib for debugging worker crashes. * Manually upgrade to pytorch 1.9.0 * -y * torch=1.9.0 * install torch on driver * Add an RLlib Tune experiment to UserTest suite. * Add ray.init() * Move example script to example/tune/, so it can be imported as module. * add __init__.py so our new module will get included in python wheel. * Add block device to RLlib test instances. * Reduce disk size a little bit. * Add metrics reporting * Allow max of 5 workers to accomodate all the worker tasks. * revert disk size change. * Minor updates * Trigger build * set max num workers * Add a compute cfg for autoscaled cpu and gpu nodes. * use 1gpu instance. * install tblib for debugging worker crashes. * Manually upgrade to pytorch 1.9.0 * -y * torch=1.9.0 * install torch on driver * bump timeout * Write a more informational result dict. * Revert changes to compute config files that are not used. * add smoke test * update * reduce timeout * Reduce the # of env per worker to 1. * Small fix for getting trial_states * Trigger build * simply result dict * lint * more lint * fix smoke test Co-authored-by: Amog Kamsetty <amogkamsetty@yahoo.com>
2021-11-03 17:04:27 -07:00
py_test(
name = "examples/tune/framework",
main = "examples/tune/framework.py",
tags = ["team:rllib", "exclusive", "examples", "examples_F"],
[RLlib] Add an RLlib Tune experiment to UserTest suite. (#19807) * Add an RLlib Tune experiment to UserTest suite. * Add ray.init() * Move example script to example/tune/, so it can be imported as module. * add __init__.py so our new module will get included in python wheel. * Add block device to RLlib test instances. * Reduce disk size a little bit. * Add metrics reporting * Allow max of 5 workers to accomodate all the worker tasks. * revert disk size change. * Minor updates * Trigger build * set max num workers * Add a compute cfg for autoscaled cpu and gpu nodes. * use 1gpu instance. * install tblib for debugging worker crashes. * Manually upgrade to pytorch 1.9.0 * -y * torch=1.9.0 * install torch on driver * Add an RLlib Tune experiment to UserTest suite. * Add ray.init() * Move example script to example/tune/, so it can be imported as module. * add __init__.py so our new module will get included in python wheel. * Add block device to RLlib test instances. * Reduce disk size a little bit. * Add metrics reporting * Allow max of 5 workers to accomodate all the worker tasks. * revert disk size change. * Minor updates * Trigger build * set max num workers * Add a compute cfg for autoscaled cpu and gpu nodes. * use 1gpu instance. * install tblib for debugging worker crashes. * Manually upgrade to pytorch 1.9.0 * -y * torch=1.9.0 * install torch on driver * bump timeout * Write a more informational result dict. * Revert changes to compute config files that are not used. * add smoke test * update * reduce timeout * Reduce the # of env per worker to 1. * Small fix for getting trial_states * Trigger build * simply result dict * lint * more lint * fix smoke test Co-authored-by: Amog Kamsetty <amogkamsetty@yahoo.com>
2021-11-03 17:04:27 -07:00
size = "medium",
srcs = ["examples/tune/framework.py"],
args = ["--smoke-test"]
)
py_test(
name = "examples/two_trainer_workflow_tf",
main = "examples/two_trainer_workflow.py",
tags = ["team:rllib", "exclusive", "examples", "examples_T"],
size = "medium",
srcs = ["examples/two_trainer_workflow.py"],
args = ["--as-test", "--stop-reward=450.0"]
)
py_test(
name = "examples/two_trainer_workflow_torch",
main = "examples/two_trainer_workflow.py",
tags = ["team:rllib", "exclusive", "examples", "examples_T"],
size = "medium",
srcs = ["examples/two_trainer_workflow.py"],
args = ["--as-test", "--torch", "--stop-reward=450.0"]
)
py_test(
name = "examples/two_trainer_workflow_mixed_torch_tf",
main = "examples/two_trainer_workflow.py",
tags = ["team:rllib", "exclusive", "examples", "examples_T"],
size = "medium",
srcs = ["examples/two_trainer_workflow.py"],
args = ["--as-test", "--mixed-torch-tf", "--stop-reward=450.0"]
)
py_test(
name = "examples/two_step_game_pg_tf",
main = "examples/two_step_game.py",
tags = ["team:rllib", "exclusive", "examples", "examples_T"],
size = "medium",
srcs = ["examples/two_step_game.py"],
args = ["--as-test", "--stop-reward=7", "--run=PG"]
)
py_test(
name = "examples/two_step_game_pg_torch",
main = "examples/two_step_game.py",
tags = ["team:rllib", "exclusive", "examples", "examples_T"],
size = "medium",
srcs = ["examples/two_step_game.py"],
args = ["--as-test", "--framework=torch", "--stop-reward=7", "--run=PG"]
)
py_test(
name = "examples/bandit/lin_ts_train_wheel_env",
main = "examples/bandit/lin_ts_train_wheel_env.py",
tags = ["team:rllib", "exclusive", "examples"],
size = "small",
srcs = ["examples/bandit/lin_ts_train_wheel_env.py"],
)
py_test(
name = "examples/bandit/tune_lin_ts_train_wheel_env",
main = "examples/bandit/tune_lin_ts_train_wheel_env.py",
tags = ["team:rllib", "exclusive", "examples"],
size = "small",
srcs = ["examples/bandit/tune_lin_ts_train_wheel_env.py"],
)
py_test(
name = "examples/bandit/tune_lin_ucb_train_recommendation",
main = "examples/bandit/tune_lin_ucb_train_recommendation.py",
tags = ["team:rllib","exclusive", "examples", ],
size = "small",
srcs = ["examples/bandit/tune_lin_ucb_train_recommendation.py"],
)
py_test(
name = "examples/bandit/tune_lin_ucb_train_recsim_env",
main = "examples/bandit/tune_lin_ucb_train_recsim_env.py",
tags = ["team:rllib", "exclusive", "examples", ],
size = "small",
srcs = ["examples/bandit/tune_lin_ucb_train_recsim_env.py"],
)
# --------------------------------------------------------------------
# examples/documentation directory
#
# Tag: documentation
#
# NOTE: Add tests alphabetically to this list.
# --------------------------------------------------------------------
py_test(
name = "examples/documentation/replay_buffer_demo",
main = "examples/documentation/replay_buffer_demo.py",
tags = ["team:rllib", "documentation", "no_main"],
size = "medium",
srcs = ["examples/documentation/replay_buffer_demo.py"],
)
py_test(
name = "examples/documentation/custom_gym_env",
main = "examples/documentation/custom_gym_env.py",
tags = ["team:rllib", "documentation", "no_main"],
size = "medium",
srcs = ["examples/documentation/custom_gym_env.py"],
)
py_test(
name = "examples/documentation/rllib_in_60s",
main = "examples/documentation/rllib_in_60s.py",
tags = ["team:rllib", "documentation", "no_main"],
size = "medium",
srcs = ["examples/documentation/rllib_in_60s.py"],
)
py_test(
name = "examples/documentation/rllib_on_ray_readme",
main = "examples/documentation/rllib_on_ray_readme.py",
tags = ["team:rllib", "documentation", "no_main"],
size = "medium",
srcs = ["examples/documentation/rllib_on_ray_readme.py"],
)
py_test(
name = "examples/documentation/rllib_on_rllib_readme",
main = "examples/documentation/rllib_on_rllib_readme.py",
tags = ["team:rllib", "documentation", "no_main"],
size = "medium",
srcs = ["examples/documentation/rllib_on_rllib_readme.py"],
)
# --------------------------------------------------------------------
# Manual/disabled tests
# --------------------------------------------------------------------
py_test_module_list(
files = [
"tests/test_dnc.py",
"tests/test_perf.py",
"tests/test_vector_env.py",
"env/tests/test_multi_agent_env.py",
"env/wrappers/tests/test_kaggle_wrapper.py",
"examples/env/tests/test_coin_game_non_vectorized_env.py",
"examples/env/tests/test_coin_game_vectorized_env.py",
"examples/env/tests/test_matrix_sequential_social_dilemma.py",
"examples/env/tests/test_wrappers.py",
"execution/tests/test_mixin_multi_agent_replay_buffer.py",
"utils/tests/test_check_env.py",
"utils/tests/test_check_multi_agent.py",
"utils/tests/test_errors.py",
"utils/tests/test_utils.py",
],
size = "large",
extra_srcs = [],
deps = [],
tags = ["manual", "team:rllib", "no_main"],
)