wehub-resource-sync
56 lines
2.0 KiB
YAML
56 lines
2.0 KiB
YAML
# @OldAPIStack
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# Run e.g. on a g3.16xlarge (4 GPUs) with `num_gpus=1` (1 for each trial;
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# MsPacman torch + tf; Pong torch + tf).
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# Uses the hyperparameters published in [2] (see rllib/algorithms/sac/README.md).
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atari-sac-tf-and-torch:
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env:
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grid_search:
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- ale_py:ALE/MsPacman-v5
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- ale_py:ALE/Pong-v5
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run: SAC
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stop:
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timesteps_total: 20000000
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config:
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# Works for both torch and tf.
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framework:
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grid_search: [tf, torch]
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env_config:
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frameskip: 1 # no frameskip
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gamma: 0.99
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q_model_config:
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hidden_activation: relu
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hidden_layer_sizes: [512]
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policy_model_config:
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hidden_activation: relu
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hidden_layer_sizes: [512]
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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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# auto = 0.98 * -log(1/|A|)
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target_entropy: auto
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clip_rewards: 1.0
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n_step: 1
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rollout_fragment_length: 1
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replay_buffer_config:
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type: MultiAgentPrioritizedReplayBuffer
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capacity: 1000000
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# How many steps of the model to sample before learning starts.
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# If True prioritized replay buffer will be used.
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prioritized_replay_alpha: 0.6
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prioritized_replay_beta: 0.4
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prioritized_replay_eps: 1e-6
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num_steps_sampled_before_learning_starts: 100000
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train_batch_size: 64
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min_sample_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. We use 100k here for the longer Atari
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# runs (DQN style: filling up the buffer a bit before learning).
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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_env_runners: 0
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num_gpus: 1
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metrics_num_episodes_for_smoothing: 5
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