ray/rllib/tuned_examples/pg/cartpole-crashing-pg.yaml
2022-07-27 00:02:18 -07:00

33 lines
1.3 KiB
YAML

cartpole-crashing-pg:
env: ray.rllib.examples.env.cartpole_crashing.CartPoleCrashing
run: PG
stop:
evaluation/episode_reward_mean: 180.0
num_env_steps_sampled: 150000
config:
# Works for both torch and tf.
framework: tf
env_config:
config:
# Crash roughly every 300 ts. This should be ok to measure 180.0
# reward (episodes are 200 ts long).
p_crash: 0.0025 # prob to crash during step()
p_crash_reset: 0.01 # prob to crash during reset()
# Time for the env to initialize when newly created.
# Every time a remote sub-environment crashes, a new env is created
# in its place and will take this long (sleep) to "initialize".
init_time_s: 1.0
horizon: 200
num_workers: 2
num_envs_per_worker: 5
# Switch on resiliency for failed sub environments (within a vectorized stack).
restart_failed_sub_environments: true
evaluation_num_workers: 2
evaluation_interval: 1
evaluation_duration: 20
evaluation_duration_unit: episodes
evaluation_parallel_to_training: true
evaluation_config:
explore: false