mirror of
https://github.com/vale981/ray
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46 lines
1.7 KiB
YAML
46 lines
1.7 KiB
YAML
cartpole-crashing-pg:
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env: ray.rllib.examples.env.cartpole_crashing.CartPoleCrashing
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run: PG
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stop:
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evaluation/episode_reward_mean: 150.0
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num_env_steps_sampled: 150000
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config:
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# Works for both torch and tf.
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framework: tf
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env_config:
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config:
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# Crash roughly every 300 ts. This should be ok to measure 180.0
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# reward (episodes are 200 ts long).
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p_crash: 0.0025 # prob to crash during step()
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p_crash_reset: 0.01 # prob to crash during reset()
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# Time for the env to initialize when newly created.
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# Every time a remote sub-environment crashes, a new env is created
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# in its place and will take this long (sleep) to "initialize".
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init_time_s: 1.0
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horizon: 200
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num_workers: 2
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num_envs_per_worker: 5
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# Disable env checking. Env checker doesn't handle Exceptions from
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# user envs, and will crash rollout worker.
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disable_env_checking: true
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# Switch on resiliency for failed sub environments (within a vectorized stack).
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restart_failed_sub_environments: true
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evaluation_num_workers: 2
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evaluation_interval: 1
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evaluation_duration: 20
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evaluation_duration_unit: episodes
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evaluation_parallel_to_training: true
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evaluation_config:
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explore: false
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env_config:
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config:
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# Make eval workers solid.
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# This test is to prove that we can learn with crashing env,
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# not eval with crashing env.
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p_crash: 0.0
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p_crash_reset: 0.0
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init_time_s: 0.0
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