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https://github.com/vale981/ray
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42 lines
1.4 KiB
YAML
42 lines
1.4 KiB
YAML
# Our implementation of SAC discrete can reach up
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# to ~750 reward in 40k timesteps. Run e.g. on a g3.4xlarge with `num_gpus=1`.
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# Uses the hyperparameters published in [2] (see rllib/agents/sac/README.md).
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mspacman-sac-tf:
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env: MsPacmanNoFrameskip-v4
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run: SAC
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stop:
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episode_reward_mean: 800
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timesteps_total: 100000
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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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gamma: 0.99
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Q_model:
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fcnet_hiddens: [512]
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fcnet_activation: relu
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policy_model:
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fcnet_hiddens: [512]
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fcnet_activation: relu
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# Do hard syncs.
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# Soft-syncs seem to work less reliably for discrete action spaces.
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tau: 1.0
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target_network_update_freq: 8000
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# paper uses: 0.98 * -log(1/|A|)
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target_entropy: 1.755
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clip_rewards: 1.0
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no_done_at_end: False
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n_step: 1
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rollout_fragment_length: 1
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prioritized_replay: true
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train_batch_size: 64
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timesteps_per_iteration: 4
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# Paper uses 20k random timesteps, which is not exactly the same, but
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# seems to work nevertheless.
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learning_starts: 20000
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optimization:
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actor_learning_rate: 0.0003
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critic_learning_rate: 0.0003
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entropy_learning_rate: 0.0003
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num_workers: 0
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num_gpus: 0
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metrics_smoothing_episodes: 5
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