# -------------------------------------------------------------------- # BAZEL/Travis-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: # a) Learning tests/regression, tagged: "learning_tests" # b) Quick agent compilation/tune-train tests, tagged "quick_train" # c-e) Utils, Models, Agents, tagged "utils", "models", and "agents_dir". # f) Tests directory (everything in rllib/tests/...), tagged: "tests_dir" # g) Examples directory (everything in rllib/examples/...), tagged: "examples" # 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 travis, which doesn't like tests that run for too long # (problems: 10min timeout, not respecting ray/ci/keep_alive.sh, or even # `travis_wait n`, etc..). # Our travis.yml file executes all these tests in 6 different jobs, which are: # 1) everything in a) using tf2.x # 2) everything in a) using tf1.x # 3) everything in b) c) d) and e) # 4) everything in g) # 5) f), BUT only those tagged `tests_dir_A` to `tests_dir_I` # 6) f), BUT only those tagged `tests_dir_J` to `tests_dir_Z` # -------------------------------------------------------------------- # Agents learning regression tests. # # Tag: learning_tests # # This will test all yaml files (via `rllib train`) # inside rllib/tuned_examples/regression_tests for actual learning success. # -------------------------------------------------------------------- py_test( name = "run_regression_tests_cartpole_tf", main = "tests/run_regression_tests.py", tags = ["learning_tests_tf", "learning_tests_cartpole"], size = "enormous", # = 60min timeout srcs = ["tests/run_regression_tests.py"], data = glob(["tuned_examples/regression_tests/cartpole-*-tf.yaml"]), # Pass `BAZEL` option and the path to look for yaml regression files. args = ["BAZEL", "tuned_examples/regression_tests"] ) py_test( name = "run_regression_tests_cartpole_torch", main = "tests/run_regression_tests.py", tags = ["learning_tests_torch", "learning_tests_cartpole"], size = "enormous", # = 60min timeout srcs = ["tests/run_regression_tests.py"], data = glob(["tuned_examples/regression_tests/cartpole-*-torch.yaml"]), # Pass `BAZEL` option and the path to look for yaml regression files. args = ["BAZEL", "tuned_examples/regression_tests"] ) py_test( name = "run_regression_tests_pendulum_tf", main = "tests/run_regression_tests.py", tags = ["learning_tests_tf", "learning_tests_pendulum"], size = "enormous", # = 60min timeout srcs = ["tests/run_regression_tests.py"], data = glob(["tuned_examples/regression_tests/pendulum-*-tf.yaml"]), # Pass `BAZEL` option and the path to look for yaml regression files. args = ["BAZEL", "tuned_examples/regression_tests"] ) py_test( name = "run_regression_tests_pendulum_torch", main = "tests/run_regression_tests.py", tags = ["learning_tests_torch", "learning_tests_pendulum"], size = "enormous", # = 60min timeout srcs = ["tests/run_regression_tests.py"], data = glob(["tuned_examples/regression_tests/pendulum-*-torch.yaml"]), # Pass `BAZEL` option and the path to look for yaml regression files. args = ["BAZEL", "tuned_examples/regression_tests"] ) # -------------------------------------------------------------------- # Agents (Compilation, Losses, simple agent functionality tests) # rllib/agents/ # # Tag: agents_dir # -------------------------------------------------------------------- # A2CTrainer py_test( name = "test_a2c", tags = ["agents_dir"], size = "small", srcs = ["agents/a3c/tests/test_a2c.py"] ) # APEXTrainer (DQN) py_test( name = "test_apex_dqn", tags = ["agents_dir"], size = "large", srcs = ["agents/dqn/tests/test_apex_dqn.py"] ) # APEXDDPGTrainer py_test( name = "test_apex_ddpg", tags = ["agents_dir"], size = "small", srcs = ["agents/ddpg/tests/test_apex_ddpg.py"] ) # ARS py_test( name = "test_ars", tags = ["agents_dir_X"], size = "medium", srcs = ["agents/ars/tests/test_ars.py"] ) # DDPGTrainer py_test( name = "test_ddpg", tags = ["agents_dir"], size = "medium", srcs = ["agents/ddpg/tests/test_ddpg.py"] ) # DQNTrainer/SimpleQTrainer py_test( name = "test_dqn", tags = ["agents_dir"], size = "medium", srcs = ["agents/dqn/tests/test_dqn.py"] ) py_test( name = "test_simple_q", tags = ["agents_dir"], size = "medium", srcs = ["agents/dqn/tests/test_simple_q.py"] ) # ES py_test( name = "test_es", tags = ["agents_dir"], size = "medium", srcs = ["agents/es/tests/test_es.py"] ) # IMPALA py_test( name = "test_impala", tags = ["agents_dir"], size = "medium", srcs = ["agents/impala/tests/test_impala.py"] ) py_test( name = "test_vtrace", tags = ["agents_dir"], size = "small", srcs = ["agents/impala/tests/test_vtrace.py"] ) # MARWILTrainer py_test( name = "test_marwil", tags = ["agents_dir"], size = "small", srcs = ["agents/marwil/tests/test_marwil.py"] ) # PGTrainer py_test( name = "test_pg", tags = ["agents_dir"], size = "small", srcs = ["agents/pg/tests/test_pg.py"] ) # PPOTrainer py_test( name = "test_ppo", tags = ["agents_dir"], size = "large", srcs = ["agents/ppo/tests/test_ppo.py", "agents/ppo/tests/test.py"] # TODO(sven): Move down once PR 6889 merged ) # DDPPO py_test( name = "test_ddppo", tags = ["agents_dir"], size = "small", srcs = ["agents/ppo/tests/test_ddppo.py"] ) # APPO py_test( name = "test_appo", tags = ["agents_dir"], size = "medium", srcs = ["agents/ppo/tests/test_appo.py"] ) # SAC py_test( name = "test_sac", tags = ["agents_dir"], size = "large", srcs = ["agents/sac/tests/test_sac.py"] ) # TD3Trainer py_test( name = "test_td3", tags = ["agents_dir"], size = "medium", srcs = ["agents/ddpg/tests/test_td3.py"] ) # -------------------------------------------------------------------- # contrib Agents # -------------------------------------------------------------------- py_test( name = "random_agent", tags = ["agents_dir"], main = "contrib/random_agent/random_agent.py", size = "small", srcs = ["contrib/random_agent/random_agent.py"] ) py_test( name = "alpha_zero_cartpole", tags = ["agents_dir"], main = "contrib/alpha_zero/examples/train_cartpole.py", size = "large", srcs = ["contrib/alpha_zero/examples/train_cartpole.py"], args = ["--training-iteration=1", "--num-workers=2", "--ray-num-cpus=3"] ) # -------------------------------------------------------------------- # 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_tf_cartpole_v0", main = "train.py", srcs = ["train.py"], tags = ["quick_train"], args = [ "--env", "CartPole-v0", "--run", "A3C", "--stop", "'{\"training_iteration\": 1}'", "--config", "'{\"num_workers\": 2}'", "--ray-num-cpus", "4" ] ) py_test( name = "test_a3c_torch_cartpole_v1", main = "train.py", srcs = ["train.py"], tags = ["quick_train"], args = [ "--torch", "--env", "CartPole-v1", "--run", "A3C", "--stop", "'{\"training_iteration\": 1}'", "--config", "'{\"num_workers\": 2, \"sample_async\": false}'", "--ray-num-cpus", "4" ] ) py_test( name = "test_a3c_tf_cartpole_v1_lstm", main = "train.py", srcs = ["train.py"], tags = ["quick_train"], args = [ "--env", "CartPole-v1", "--run", "A3C", "--stop", "'{\"training_iteration\": 1}'", "--config", "'{\"num_workers\": 2, \"model\": {\"use_lstm\": true}}'", "--ray-num-cpus", "4" ] ) py_test( name = "test_a3c_torch_pendulum_v0", main = "train.py", srcs = ["train.py"], size = "small", tags = ["quick_train"], args = [ "--torch", "--env", "Pendulum-v0", "--run", "A3C", "--stop", "'{\"training_iteration\": 1}'", "--config", "'{\"num_workers\": 2, \"sample_async\": false}'", "--ray-num-cpus", "4" ] ) py_test( name = "test_a3c_tf_pong_deterministic_v0", main = "train.py", srcs = ["train.py"], tags = ["quick_train"], args = [ "--env", "PongDeterministic-v0", "--run", "A3C", "--stop", "'{\"training_iteration\": 1}'", "--config", "'{\"num_workers\": 2}'", "--ray-num-cpus", "4" ] ) py_test( name = "test_a3c_torch_pong_deterministic_v0", main = "train.py", srcs = ["train.py"], tags = ["quick_train"], args = [ "--torch", "--env", "PongDeterministic-v0", "--run", "A3C", "--stop", "'{\"training_iteration\": 1}'", "--config", "'{\"num_workers\": 2, \"sample_async\": false}'", "--ray-num-cpus", "4" ] ) py_test( name = "test_a3c_torch_pong_deterministic_v4", main = "train.py", srcs = ["train.py"], tags = ["quick_train"], args = [ "--torch", "--env", "PongDeterministic-v0", "--run", "A3C", "--stop", "'{\"training_iteration\": 1}'", "--config", "'{\"num_workers\": 2, \"use_pytorch\": true, \"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 = ["quick_train"], args = [ "--env", "Pong-ram-v4", "--run", "A3C", "--stop", "'{\"training_iteration\": 1}'", "--config", "'{\"num_workers\": 2}'", "--ray-num-cpus", "4" ] ) py_test( name = "test_a2c_tf_pong_deterministic_v0", main = "train.py", srcs = ["train.py"], tags = ["quick_train"], args = [ "--env", "PongDeterministic-v0", "--run", "A2C", "--stop", "'{\"training_iteration\": 1}'", "--config", "'{\"num_workers\": 2}'", "--ray-num-cpus", "4" ] ) # DDPG/APEX-DDPG/TD3 py_test( name = "test_ddpg_pendulum_v0", main = "train.py", srcs = ["train.py"], tags = ["quick_train"], args = [ "--env", "Pendulum-v0", "--run", "DDPG", "--stop", "'{\"training_iteration\": 1}'", "--config", "'{\"num_workers\": 1}'" ] ) py_test( name = "test_ddpg_mountaincar_continuous_v0_num_workers_0", main = "train.py", srcs = ["train.py"], tags = ["quick_train"], args = [ "--env", "MountainCarContinuous-v0", "--run", "DDPG", "--stop", "'{\"training_iteration\": 1}'", "--config", "'{\"num_workers\": 0}'" ] ) py_test( name = "test_ddpg_mountaincar_continuous_v0_num_workers_1", main = "train.py", srcs = ["train.py"], tags = ["quick_train"], args = [ "--env", "MountainCarContinuous-v0", "--run", "DDPG", "--stop", "'{\"training_iteration\": 1}'", "--config", "'{\"num_workers\": 1}'" ] ) py_test( name = "test_apex_ddpg_pendulum_v0", main = "train.py", srcs = ["train.py"], tags = ["quick_train"], args = [ "--env", "Pendulum-v0", "--run", "APEX_DDPG", "--ray-num-cpus", "8", "--stop", "'{\"training_iteration\": 1}'", "--config", "'{\"num_workers\": 2, \"optimizer\": {\"num_replay_buffer_shards\": 1}, \"learning_starts\": 100, \"min_iter_time_s\": 1}'", "--ray-num-cpus", "4" ] ) py_test( name = "test_apex_ddpg_pendulum_v0_complete_episode_batches", main = "train.py", srcs = ["train.py"], tags = ["quick_train"], args = [ "--env", "Pendulum-v0", "--run", "APEX_DDPG", "--stop", "'{\"training_iteration\": 1}'", "--config", "'{\"num_workers\": 2, \"optimizer\": {\"num_replay_buffer_shards\": 1}, \"learning_starts\": 100, \"min_iter_time_s\": 1, \"batch_mode\": \"complete_episodes\"}'", "--ray-num-cpus", "4", ] ) py_test( name = "test_td3_pendulum_v0", main = "train.py", srcs = ["train.py"], tags = ["quick_train"], args = [ "--env", "Pendulum-v0", "--run", "TD3", "--stop", "'{\"training_iteration\": 1}'", "--config", "'{\"num_workers\": 1}'" ] ) # DQN/APEX py_test( name = "test_dqn_frozenlake_v0", main = "train.py", srcs = ["train.py"], size = "small", tags = ["quick_train"], args = [ "--env", "FrozenLake-v0", "--run", "DQN", "--stop", "'{\"training_iteration\": 1}'" ] ) py_test( name = "test_dqn_cartpole_v0_no_dueling", main = "train.py", srcs = ["train.py"], size = "small", tags = ["quick_train"], args = [ "--env", "CartPole-v0", "--run", "DQN", "--stop", "'{\"training_iteration\": 1}'", "--config", "'{\"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 = ["quick_train"], args = [ "--env", "CartPole-v0", "--run", "DQN", "--stop", "'{\"training_iteration\": 1}'", "--config", "'{\"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 = ["quick_train", "external_files"], size = "small", # Include the json data file. data = glob(["tests/data/cartpole_small/**"]), args = [ "--env", "CartPole-v0", "--run", "DQN", "--stop", "'{\"training_iteration\": 1}'", "--config", "'{\"input\": \"tests/data/cartpole_small\", \"learning_starts\": 0, \"input_evaluation\": [\"wis\", \"is\"], \"exploration_config\": {\"type\": \"SoftQ\"}}'" ] ) py_test( name = "test_dqn_pong_deterministic_v4", main = "train.py", srcs = ["train.py"], tags = ["quick_train"], args = [ "--env", "PongDeterministic-v4", "--run", "DQN", "--stop", "'{\"training_iteration\": 1}'", "--config", "'{\"lr\": 1e-4, \"exploration_config\": {\"epsilon_timesteps\": 200000, \"final_epsilon\": 0.01}, \"buffer_size\": 10000, \"rollout_fragment_length\": 4, \"learning_starts\": 10000, \"target_network_update_freq\": 1000, \"gamma\": 0.99, \"prioritized_replay\": true}'" ] ) py_test( name = "test_apex_cartpole_v0", main = "train.py", srcs = ["train.py"], tags = ["quick_train"], args = [ "--env", "CartPole-v0", "--run", "APEX", "--stop", "'{\"training_iteration\": 1}'", "--config", "'{\"num_workers\": 2, \"timesteps_per_iteration\": 1000, \"num_gpus\": 0, \"min_iter_time_s\": 1}'", "--ray-num-cpus", "4" ] ) # ES py_test( name = "test_es_pendulum_v0", main = "train.py", srcs = ["train.py"], tags = ["quick_train"], args = [ "--env", "Pendulum-v0", "--run", "ES", "--stop", "'{\"training_iteration\": 1}'", "--config", "'{\"stepsize\": 0.01, \"episodes_per_batch\": 20, \"train_batch_size\": 100, \"num_workers\": 2}'", "--ray-num-cpus", "4" ] ) py_test( name = "test_es_pong_v0", main = "train.py", srcs = ["train.py"], tags = ["quick_train"], args = [ "--env", "Pong-v0", "--run", "ES", "--stop", "'{\"training_iteration\": 1}'", "--config", "'{\"stepsize\": 0.01, \"episodes_per_batch\": 20, \"train_batch_size\": 100, \"num_workers\": 2}'", "--ray-num-cpus", "4" ] ) # IMPALA py_test( name = "test_impala_cartpole_v0", main = "train.py", srcs = ["train.py"], tags = ["quick_train"], args = [ "--env", "CartPole-v0", "--run", "IMPALA", "--stop", "'{\"training_iteration\": 1}'", "--config", "'{\"num_gpus\": 0, \"num_workers\": 2, \"min_iter_time_s\": 1}'", "--ray-num-cpus", "4", ] ) py_test( name = "test_impala_cartpole_v0_num_aggregation_workers_2", main = "train.py", srcs = ["train.py"], tags = ["quick_train"], args = [ "--env", "CartPole-v0", "--run", "IMPALA", "--stop", "'{\"training_iteration\": 1}'", "--config", "'{\"num_gpus\": 0, \"num_workers\": 2, \"num_aggregation_workers\": 2, \"min_iter_time_s\": 1}'", "--ray-num-cpus", "5", ] ) py_test( name = "test_impala_cartpole_v0_lstm", main = "train.py", srcs = ["train.py"], tags = ["quick_train"], args = [ "--env", "CartPole-v0", "--run", "IMPALA", "--stop", "'{\"training_iteration\": 1}'", "--config", "'{\"num_gpus\": 0, \"num_workers\": 2, \"min_iter_time_s\": 1, \"model\": {\"use_lstm\": true}}'", "--ray-num-cpus", "4", ] ) py_test( name = "test_impala_buffers_2", main = "train.py", srcs = ["train.py"], tags = ["quick_train"], args = [ "--env", "CartPole-v0", "--run", "IMPALA", "--stop", "'{\"training_iteration\": 1}'", "--config", "'{\"num_gpus\": 0, \"num_workers\": 2, \"min_iter_time_s\": 1, \"num_data_loader_buffers\": 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 = ["quick_train"], args = [ "--env", "CartPole-v0", "--run", "IMPALA", "--stop", "'{\"training_iteration\": 1}'", "--config", "'{\"num_gpus\": 0, \"num_workers\": 2, \"min_iter_time_s\": 1, \"num_data_loader_buffers\": 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 = ["quick_train"], args = [ "--env", "PongDeterministic-v4", "--run", "IMPALA", "--stop", "'{\"timesteps_total\": 40000}'", "--ray-object-store-memory=1000000000", "--config", "'{\"num_workers\": 1, \"num_gpus\": 0, \"num_envs_per_worker\": 32, \"rollout_fragment_length\": 50, \"train_batch_size\": 50, \"learner_queue_size\": 1}'" ] ) # MARWIL py_test( name = "test_marwil_cartpole_v0_tf", main = "train.py", srcs = ["train.py"], tags = ["quick_train", "external_files"], size = "small", # Include the json data file. data = glob(["tests/data/cartpole_small/**"]), args = [ "--env", "CartPole-v0", "--run", "MARWIL", "--stop", "'{\"training_iteration\": 1}'", "--config", "'{\"input\": \"tests/data/cartpole_small\", \"learning_starts\": 0, \"input_evaluation\": [\"wis\", \"is\"], \"shuffle_buffer_size\": 10}'" ] ) py_test( name = "test_marwil_cartpole_v0_torch", main = "train.py", srcs = ["train.py"], tags = ["quick_train", "external_files"], size = "small", # Include the json data file. data = glob(["tests/data/cartpole_small/**"]), args = [ "--env", "CartPole-v0", "--run", "MARWIL", "--stop", "'{\"training_iteration\": 1}'", "--config", "'{\"use_pytorch\": true, \"input\": \"tests/data/cartpole_small\", \"learning_starts\": 0, \"input_evaluation\": [\"wis\", \"is\"], \"shuffle_buffer_size\": 10}'" ] ) # PG py_test( name = "test_pg_tf_frozenlake_v0", main = "train.py", srcs = ["train.py"], tags = ["quick_train"], args = [ "--env", "FrozenLake-v0", "--run", "PG", "--stop", "'{\"training_iteration\": 1}'", "--config", "'{\"rollout_fragment_length\": 500, \"num_workers\": 1}'" ] ) py_test( name = "test_pg_torch_frozenlake_v0", main = "train.py", srcs = ["train.py"], size = "small", tags = ["quick_train"], args = [ "--torch", "--env", "FrozenLake-v0", "--run", "PG", "--stop", "'{\"training_iteration\": 1}'", "--config", "'{\"rollout_fragment_length\": 500, \"num_workers\": 1}'" ] ) py_test( name = "test_pg_tf_cartpole_v0", main = "train.py", srcs = ["train.py"], size = "small", tags = ["quick_train"], args = [ "--env", "CartPole-v0", "--run", "PG", "--stop", "'{\"training_iteration\": 1}'", "--config", "'{\"rollout_fragment_length\": 500, \"num_workers\": 1}'" ] ) py_test( name = "test_pg_torch_cartpole_v0", main = "train.py", srcs = ["train.py"], tags = ["quick_train"], args = [ "--torch", "--env", "CartPole-v0", "--run", "PG", "--stop", "'{\"training_iteration\": 1}'", "--config", "'{\"rollout_fragment_length\": 500}'" ] ) py_test( name = "test_pg_tf_cartpole_v0_lstm", main = "train.py", srcs = ["train.py"], tags = ["quick_train"], args = [ "--env", "CartPole-v0", "--run", "PG", "--stop", "'{\"training_iteration\": 1}'", "--config", "'{\"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 = ["quick_train"], args = [ "--env", "CartPole-v0", "--run", "PG", "--stop", "'{\"training_iteration\": 1}'", "--config", "'{\"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 = ["quick_train"], args = [ "--env", "Pong-v0", "--run", "PG", "--stop", "'{\"training_iteration\": 1}'", "--config", "'{\"rollout_fragment_length\": 500, \"num_workers\": 1}'" ] ) # PPO/APPO py_test( name = "test_ppo_tf_frozenlake_v0", main = "train.py", srcs = ["train.py"], size = "small", tags = ["quick_train"], args = [ "--env", "FrozenLake-v0", "--run", "PPO", "--stop", "'{\"training_iteration\": 1}'", "--config", "'{\"num_sgd_iter\": 10, \"sgd_minibatch_size\": 64, \"train_batch_size\": 1000, \"num_workers\": 1}'" ] ) py_test( name = "test_ppo_torch_frozenlake_v0", main = "train.py", srcs = ["train.py"], size = "small", tags = ["quick_train"], args = [ "--torch", "--env", "FrozenLake-v0", "--run", "PPO", "--stop", "'{\"training_iteration\": 1}'", "--config", "'{\"num_sgd_iter\": 10, \"sgd_minibatch_size\": 64, \"train_batch_size\": 1000, \"num_workers\": 1}'" ] ) py_test( name = "test_ppo_tf_cartpole_v1", main = "train.py", srcs = ["train.py"], tags = ["quick_train"], args = [ "--env", "CartPole-v1", "--run", "PPO", "--stop", "'{\"training_iteration\": 1}'", "--config", "'{\"kl_coeff\": 1.0, \"num_sgd_iter\": 10, \"lr\": 1e-4, \"sgd_minibatch_size\": 64, \"train_batch_size\": 2000, \"num_workers\": 1, \"model\": {\"free_log_std\": true}}'" ] ) py_test( name = "test_ppo_torch_cartpole_v1", main = "train.py", srcs = ["train.py"], size = "small", tags = ["quick_train"], args = [ "--torch", "--env", "CartPole-v1", "--run", "PPO", "--stop", "'{\"training_iteration\": 1}'", "--config", "'{\"kl_coeff\": 1.0, \"num_sgd_iter\": 10, \"lr\": 1e-4, \"sgd_minibatch_size\": 64, \"train_batch_size\": 2000, \"num_workers\": 1, \"model\": {\"free_log_std\": true}}'" ] ) py_test( name = "test_ppo_tf_cartpole_v1_lstm", main = "train.py", srcs = ["train.py"], tags = ["quick_train"], args = [ "--env", "CartPole-v1", "--run", "PPO", "--stop", "'{\"training_iteration\": 1}'", "--config", "'{\"simple_optimizer\": false, \"num_sgd_iter\": 2, \"model\": {\"use_lstm\": true}}'", "--ray-num-cpus", "4" ] ) py_test( name = "test_ppo_tf_cartpole_v1_lstm_simple_optimizer", main = "train.py", srcs = ["train.py"], tags = ["quick_train"], args = [ "--env", "CartPole-v1", "--run", "PPO", "--stop", "'{\"training_iteration\": 1}'", "--config", "'{\"simple_optimizer\": true, \"num_sgd_iter\": 2, \"model\": {\"use_lstm\": true}}'", "--ray-num-cpus", "4" ] ) # TODO(sven): Fix LSTM auto-wrapping for torch models. This test case did not(!) exist in Jenkins. #py_test( # name = "test_ppo_torch_cartpole_v1_lstm_simple_optimizer", # main = "train.py", srcs = ["train.py"], # args = [ # "--torch", # "--env", "CartPole-v1", # "--run", "PPO", # "--stop", "'{\"training_iteration\": 1}'", # "--config", "'{\"simple_optimizer\": true, \"num_sgd_iter\": 2, \"model\": {\"use_lstm\": true}}'", # "--ray-num-cpus", "4" # ] #) py_test( name = "test_ppo_tf_cartpole_v1_complete_episode_batches", main = "train.py", srcs = ["train.py"], tags = ["quick_train"], args = [ "--env", "CartPole-v1", "--run", "PPO", "--stop", "'{\"training_iteration\": 1}'", "--config", "'{\"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 = ["quick_train"], args = [ "--env", "CartPole-v1", "--run", "PPO", "--stop", "'{\"training_iteration\": 1}'", "--config", "'{\"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 = ["quick_train"], args = [ "--env", "CartPole-v1", "--run", "PPO", "--stop", "'{\"training_iteration\": 2}'", "--config", "'{\"remote_worker_envs\": true, \"num_envs_per_worker\": 2, \"num_workers\": 1, \"train_batch_size\": 100, \"sgd_minibatch_size\": 50}'" ] ) py_test( name = "test_ppo_tf_montezuma_revenge_v0", main = "train.py", srcs = ["train.py"], tags = ["quick_train"], args = [ "--env", "MontezumaRevenge-v0", "--run", "PPO", "--stop", "'{\"training_iteration\": 1}'", "--config", "'{\"kl_coeff\": 1.0, \"num_sgd_iter\": 10, \"lr\": 1e-4, \"sgd_minibatch_size\": 64, \"train_batch_size\": 2000, \"num_workers\": 1, \"model\": {\"dim\": 40, \"conv_filters\": [[16, [8, 8], 4], [32, [4, 4], 2], [512, [5, 5], 1]]}}'" ] ) py_test( name = "test_ppo_torch_montezuma_revenge_v0", main = "train.py", srcs = ["train.py"], tags = ["quick_train"], args = [ "--torch", "--env", "MontezumaRevenge-v0", "--run", "PPO", "--stop", "'{\"training_iteration\": 1}'", "--config", "'{\"kl_coeff\": 1.0, \"num_sgd_iter\": 10, \"lr\": 1e-4, \"sgd_minibatch_size\": 64, \"train_batch_size\": 2000, \"num_workers\": 1, \"model\": {\"dim\": 40, \"conv_filters\": [[16, [8, 8], 4], [32, [4, 4], 2], [512, [5, 5], 1]]}}'" ] ) py_test( name = "test_appo_tf_pendulum_v0_no_gpus", main = "train.py", srcs = ["train.py"], tags = ["quick_train"], args = [ "--env", "Pendulum-v0", "--run", "APPO", "--stop", "'{\"training_iteration\": 1}'", "--config", "'{\"num_workers\": 2, \"num_gpus\": 0}'", "--ray-num-cpus", "4" ] ) # -------------------------------------------------------------------- # Models and Distributions # rllib/models/ # # Tag: models # -------------------------------------------------------------------- py_test( name = "test_distributions", tags = ["models"], size = "small", srcs = ["models/tests/test_distributions.py"] ) # -------------------------------------------------------------------- # Optimizers and Memories # rllib/optimizers/ # # Tag: optimizers # -------------------------------------------------------------------- py_test( name = "test_optimizers", tags = ["optimizers"], size = "large", srcs = ["optimizers/tests/test_optimizers.py"] ) py_test( name = "test_segment_tree", tags = ["optimizers"], size = "small", srcs = ["optimizers/tests/test_segment_tree.py"] ) py_test( name = "test_prioritized_replay_buffer", tags = ["optimizers"], size = "small", srcs = ["optimizers/tests/test_prioritized_replay_buffer.py"] ) # -------------------------------------------------------------------- # Policies # rllib/policy/ # # Tag: policy # -------------------------------------------------------------------- py_test( name = "policy/tests/test_compute_log_likelihoods", tags = ["policy"], size = "medium", srcs = ["policy/tests/test_compute_log_likelihoods.py"] ) # -------------------------------------------------------------------- # Utils: # rllib/utils/ # # Tag: utils # -------------------------------------------------------------------- py_test( name = "test_explorations", tags = ["utils"], size = "large", srcs = ["utils/exploration/tests/test_explorations.py"] ) py_test( name = "test_parameter_noise", tags = ["utils"], size = "small", srcs = ["utils/exploration/tests/test_parameter_noise.py"] ) # Schedules py_test( name = "test_schedules", tags = ["utils"], size = "small", srcs = ["utils/schedules/tests/test_schedules.py"] ) py_test( name = "test_framework_agnostic_components", tags = ["utils"], size = "small", data = glob(["utils/tests/**"]), srcs = ["utils/tests/test_framework_agnostic_components.py"] ) # TaskPool py_test( name = "test_taskpool", tags = ["utils"], size = "small", srcs = ["utils/tests/test_taskpool.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/test_avail_actions_qmix", tags = ["tests_dir", "tests_dir_A"], size = "small", srcs = ["tests/test_avail_actions_qmix.py"] ) py_test( name = "tests/test_catalog", tags = ["tests_dir", "tests_dir_C"], size = "small", srcs = ["tests/test_catalog.py"] ) py_test( name = "tests/test_checkpoint_restore", tags = ["tests_dir", "tests_dir_C"], size = "enormous", srcs = ["tests/test_checkpoint_restore.py"] ) py_test( name = "tests/test_dependency", tags = ["tests_dir", "tests_dir_D"], size = "small", srcs = ["tests/test_dependency.py"] ) py_test( name = "tests/test_dependency_torch", tags = ["tests_dir", "tests_dir_D"], size = "small", srcs = ["tests/test_dependency_torch.py"] ) py_test( name = "tests/test_eager_support", tags = ["tests_dir", "tests_dir_E"], size = "enormous", srcs = ["tests/test_eager_support.py"] ) py_test( name = "test_env_with_subprocess", main = "tests/test_env_with_subprocess.py", tags = ["tests_dir", "tests_dir_E"], size = "small", srcs = ["tests/test_env_with_subprocess.py"] ) py_test( name = "tests/test_evaluators", tags = ["tests_dir", "tests_dir_E"], size = "medium", srcs = ["tests/test_evaluators.py"] ) py_test( name = "tests/test_external_env", tags = ["tests_dir", "tests_dir_E"], size = "large", srcs = ["tests/test_external_env.py"] ) py_test( name = "tests/test_external_multi_agent_env", tags = ["tests_dir", "tests_dir_E"], size = "medium", srcs = ["tests/test_external_multi_agent_env.py"] ) py_test( name = "tests/test_filters", tags = ["tests_dir", "tests_dir_F"], size = "small", srcs = ["tests/test_filters.py"] ) py_test( name = "tests/test_ignore_worker_failure", tags = ["tests_dir", "tests_dir_I"], size = "large", srcs = ["tests/test_ignore_worker_failure.py"] ) py_test( name = "tests/test_io", tags = ["tests_dir", "tests_dir_I"], size = "medium", srcs = ["tests/test_io.py"] ) py_test( name = "tests/test_execution", tags = ["tests_dir", "tests_dir_E"], size = "medium", srcs = ["tests/test_execution.py"] ) py_test( name = "tests/test_local", tags = ["tests_dir", "tests_dir_L"], size = "medium", srcs = ["tests/test_local.py"] ) py_test( name = "tests/test_lstm", tags = ["tests_dir", "tests_dir_L"], size = "medium", srcs = ["tests/test_lstm.py"] ) py_test( name = "tests/test_model_imports", tags = ["tests_dir", "tests_dir_M", "model_imports"], size = "small", data = glob(["tests/data/model_weights/**"]), srcs = ["tests/test_model_imports.py"] ) py_test( name = "tests/test_multi_agent_env", tags = ["tests_dir", "tests_dir_M"], size = "large", srcs = ["tests/test_multi_agent_env.py"] ) py_test( name = "tests/test_multi_agent_pendulum", tags = ["tests_dir", "tests_dir_M"], size = "large", srcs = ["tests/test_multi_agent_pendulum.py"] ) py_test( name = "tests/test_nested_observation_spaces", main = "tests/test_nested_observation_spaces.py", tags = ["tests_dir", "tests_dir_N"], size = "small", srcs = ["tests/test_nested_observation_spaces.py"] ) py_test( name = "tests/test_exec_api", tags = ["tests_dir", "tests_dir_E"], size = "small", srcs = ["tests/test_exec_api.py"] ) py_test( name = "tests/test_reproducibility", tags = ["tests_dir", "tests_dir_R"], size = "large", srcs = ["tests/test_reproducibility.py"] ) py_test( name = "test_rollout", main = "tests/test_rollout.py", tags = ["tests_dir", "tests_dir_R"], size = "enormous", data = ["train.py", "rollout.py"], srcs = ["tests/test_rollout.py"] ) py_test( name = "tests/test_rollout_worker", tags = ["tests_dir", "tests_dir_R"], size = "large", srcs = ["tests/test_rollout_worker.py"] ) py_test( name = "tests/test_supported_spaces", tags = ["tests_dir", "tests_dir_S"], size = "enormous", srcs = ["tests/test_supported_spaces.py"] ) # -------------------------------------------------------------------- # examples/ directory # # 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/autoregressive_action_dist", main = "examples/autoregressive_action_dist.py", tags = ["examples", "examples_A"], size = "large", srcs = ["examples/autoregressive_action_dist.py"], args = ["--stop=150", "--num-cpus=4"] ) py_test( name = "examples/batch_norm_model_ppo", main="examples/batch_norm_model.py", tags = ["examples", "examples_B"], size = "medium", srcs = ["examples/batch_norm_model.py"], args = ["--run=PPO", "--num-iters=1"] ) py_test( name = "examples/batch_norm_model_pg", main="examples/batch_norm_model.py", tags = ["examples", "examples_B"], size = "medium", srcs = ["examples/batch_norm_model.py"], args = ["--run=PG", "--num-iters=1"] ) py_test( name = "examples/batch_norm_model_dqn", main="examples/batch_norm_model.py", tags = ["examples", "examples_B"], size = "medium", srcs = ["examples/batch_norm_model.py"], args = ["--run=DQN", "--num-iters=1"] ) py_test( name = "examples/batch_norm_model_ddpg", main="examples/batch_norm_model.py", tags = ["examples", "examples_B"], size = "medium", srcs = ["examples/batch_norm_model.py"], args = ["--run=DDPG", "--num-iters=1"] ) py_test( name = "examples/cartpole_lstm_impala", main="examples/cartpole_lstm.py", tags = ["examples", "examples_C"], size = "medium", srcs = ["examples/cartpole_lstm.py"], args = ["--run=IMPALA", "--stop=40", "--num-cpus=4"] ) py_test( name = "examples/cartpole_lstm_ppo", main="examples/cartpole_lstm.py", tags = ["examples", "examples_C"], size = "medium", srcs = ["examples/cartpole_lstm.py"], args = ["--run=PPO", "--stop=40", "--num-cpus=4"] ) py_test( name = "examples/cartpole_lstm_ppo_with_prev_a_and_r", main="examples/cartpole_lstm.py", tags = ["examples", "examples_C"], size = "large", srcs = ["examples/cartpole_lstm.py"], args = ["--run=PPO", "--stop=40", "--use-prev-action-reward", "--num-cpus=4"] ) py_test( name = "examples/centralized_critic", tags = ["examples", "examples_C"], size = "medium", srcs = ["examples/centralized_critic.py"], args = ["--stop=2000"] ) py_test( name = "examples/centralized_critic_2", tags = ["examples", "examples_C"], size = "medium", srcs = ["examples/centralized_critic_2.py"], args = ["--stop=2000"] ) py_test( name = "examples/custom_eval", main = "examples/custom_eval.py", tags = ["examples", "examples_C"], size = "medium", srcs = ["examples/custom_eval.py"], args = ["--custom-eval", "--num-cpus=4"] ) py_test( name = "examples/custom_keras_model_a2c", main="examples/custom_keras_model.py", tags = ["examples", "examples_C"], 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 = ["examples", "examples_C"], 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 = ["examples", "examples_C"], size = "medium", srcs = ["examples/custom_keras_model.py"], args = ["--run=PPO", "--stop=50", "--num-cpus=4"] ) py_test( name = "examples/custom_keras_rnn_model_repeat_after_me", main = "examples/custom_keras_rnn_model.py", tags = ["examples", "examples_C"], size = "large", srcs = ["examples/custom_keras_rnn_model.py"], args = ["--run=PPO", "--stop=50", "--env=RepeatAfterMeEnv", "--num-cpus=4"] ) py_test( name = "examples/custom_keras_rnn_model_repeat_initial", main = "examples/custom_keras_rnn_model.py", tags = ["examples", "examples_C"], size = "large", srcs = ["examples/custom_keras_rnn_model.py"], args = ["--run=PPO", "--stop=50", "--env=RepeatInitialObsEnv", "--num-cpus=4"] ) py_test( name = "examples/custom_loss", tags = ["examples", "examples_C"], size = "small", # Include the json data file. data = glob(["tests/data/cartpole_small/**"]), srcs = ["examples/custom_loss.py"], args = ["--iters=2", "--input-files=tests/data/cartpole_small"] ) py_test( name = "examples/custom_metrics_and_callbacks", tags = ["examples", "examples_C"], size = "small", srcs = ["examples/custom_metrics_and_callbacks.py"], args = ["--num-iters=2"] ) py_test( name = "examples/custom_metrics_and_callbacks_legacy", tags = ["examples", "examples_C"], size = "small", srcs = ["examples/custom_metrics_and_callbacks_legacy.py"], args = ["--num-iters=2"] ) py_test( name = "examples/custom_tf_policy", tags = ["examples", "examples_C"], size = "medium", srcs = ["examples/custom_tf_policy.py"], args = ["--iters=2", "--num-cpus=4"] ) py_test( name = "examples/custom_torch_rnn_model", main = "examples/custom_torch_rnn_model.py", tags = ["examples", "examples_C"], size = "medium", srcs = ["examples/custom_torch_rnn_model.py"], args = ["--run=PPO", "--stop=90", "--num-cpus=4"] ) py_test( name = "examples/custom_torch_policy", tags = ["examples", "examples_C"], size = "small", srcs = ["examples/custom_torch_policy.py"], args = ["--iters=2", "--num-cpus=4"] ) py_test( name = "examples/eager_execution", tags = ["examples", "examples_E"], size = "small", srcs = ["examples/eager_execution.py"], args = ["--iters=2"] ) py_test( name = "examples/hierarchical_training_tf", tags = ["examples", "examples_H"], size = "small", srcs = ["examples/hierarchical_training.py"], args = ["--stop-reward=0.0"] ) py_test( name = "examples/hierarchical_training_torch", tags = ["examples", "examples_H"], size = "small", srcs = ["examples/hierarchical_training.py"], args = ["--torch", "--stop-reward=0.0"] ) py_test( name = "examples/multi_agent_cartpole", tags = ["examples", "examples_M"], size = "medium", srcs = ["examples/multi_agent_cartpole.py"], args = ["--num-iters=2", "--num-cpus=4"] ) py_test( name = "examples/multi_agent_custom_policy", tags = ["examples", "examples_M"], size = "medium", srcs = ["examples/multi_agent_custom_policy.py"], ) py_test( name = "examples/multi_agent_two_trainers", tags = ["examples", "examples_M"], size = "medium", srcs = ["examples/multi_agent_two_trainers.py"], args = ["--num-iters=2"] ) py_test( name = "examples/two_trainer_workflow", tags = ["examples", "examples_T"], size = "medium", srcs = ["examples/two_trainer_workflow.py"], args = ["--num-iters=2"] ) py_test( name = "examples/nested_action_spaces_ppo", main = "examples/nested_action_spaces.py", tags = ["examples", "examples_N"], size = "medium", srcs = ["examples/nested_action_spaces.py"], args = ["--stop=-500", "--run=PPO"] ) py_test( name = "examples/parametric_actions_cartpole_pg", main = "examples/parametric_actions_cartpole.py", tags = ["examples", "examples_P"], size = "medium", srcs = ["examples/parametric_actions_cartpole.py"], args = ["--run=PG", "--stop=50"] ) py_test( name = "examples/parametric_actions_cartpole_ppo", main = "examples/parametric_actions_cartpole.py", tags = ["examples", "examples_P"], size = "medium", srcs = ["examples/parametric_actions_cartpole.py"], args = ["--run=PPO", "--stop=50"] ) py_test( name = "examples/parametric_actions_cartpole_dqn", main = "examples/parametric_actions_cartpole.py", tags = ["examples", "examples_P"], size = "medium", srcs = ["examples/parametric_actions_cartpole.py"], args = ["--run=DQN", "--stop=50"] ) py_test( name = "examples/rollout_worker_custom_workflow", tags = ["examples", "examples_R"], size = "small", srcs = ["examples/rollout_worker_custom_workflow.py"], args = ["--num-cpus=4"] ) sh_test( name = "examples/serving/test_local_inference", tags = ["examples", "examples_L", "exclusive"], size = "medium", srcs = ["examples/serving/test_local_inference.sh"], data = glob(["examples/serving/*.py"]), ) sh_test( name = "examples/serving/test_remote_inference", tags = ["examples", "examples_R", "exclusive"], size = "medium", srcs = ["examples/serving/test_remote_inference.sh"], data = glob(["examples/serving/*.py"]), ) py_test( name = "examples/rock_paper_scissors_multiagent", main = "examples/rock_paper_scissors_multiagent.py", tags = ["examples", "examples_R"], size = "large", srcs = ["examples/rock_paper_scissors_multiagent.py"], args = ["--stop=200"], ) py_test( name = "examples/twostep_game_maddpg", main = "examples/twostep_game.py", tags = ["examples", "examples_T"], size = "large", srcs = ["examples/twostep_game.py"], args = ["--stop=2000", "--run=contrib/MADDPG"] ) py_test( name = "contrib/bandits/examples/lin_ts", main = "contrib/bandits/examples/simple_context_bandit.py", tags = ["examples", "examples_T"], size = "small", srcs = ["contrib/bandits/examples/simple_context_bandit.py"], args = ["--stop-at-reward=10", "--run=contrib/LinTS"], ) py_test( name = "contrib/bandits/examples/lin_ucb", main = "contrib/bandits/examples/simple_context_bandit.py", tags = ["examples", "examples_U"], size = "small", srcs = ["contrib/bandits/examples/simple_context_bandit.py"], args = ["--stop-at-reward=10", "--run=contrib/LinUCB"], ) py_test( name = "examples/twostep_game_pg", main = "examples/twostep_game.py", tags = ["examples", "examples_T"], size = "medium", srcs = ["examples/twostep_game.py"], args = ["--stop=2000", "--run=PG"] ) py_test( name = "examples/twostep_game_qmix", main = "examples/twostep_game.py", tags = ["examples", "examples_T"], size = "medium", srcs = ["examples/twostep_game.py"], args = ["--stop=2000", "--run=QMIX"] ) py_test( name = "examples/twostep_game_apex_qmix", main = "examples/twostep_game.py", tags = ["examples", "examples_T"], size = "medium", srcs = ["examples/twostep_game.py"], args = ["--stop=2000", "--run=APEX_QMIX", "--num-cpus=4"] )