mirror of
https://github.com/vale981/ray
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83e06cd30a
* WIP. * WIP. * WIP. * WIP. * WIP. * Fix * WIP. * Add TD3 quick Pendulum regresison. * Cleanup. * Fix. * LINT. * Fix. * Sort quick_learning test cases, add TD3. * Sort quick_learning test cases, add TD3. * Revert test_checkpoint_restore.py (debugging) changes. * Fix old soft_q settings in documentation and test configs. * More doc fixes. * Fix test case. * Fix test case. * Lower test load. * WIP.
23 lines
707 B
YAML
23 lines
707 B
YAML
invertedpendulum-td3:
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# This is a TD3 with stopping conditions and network size tuned specifically
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# for InvertedPendulum. Should be able to reach 1,000 reward (the maximum
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# achievable) in 10,000 to 20,000 steps.
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env: InvertedPendulum-v2
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run: TD3
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stop:
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episode_reward_mean: 9999.9
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time_total_s: 900 # 15 minutes
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timesteps_total: 1000000
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config:
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# === Model ===
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actor_hiddens: [32, 32]
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critic_hiddens: [32, 32]
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# === Exploration ===
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learning_starts: 1000
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exploration_config:
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random_timesteps: 1000
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# === Evaluation ===
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evaluation_interval: 1
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evaluation_num_episodes: 5
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