mirror of
https://github.com/vale981/ray
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31 lines
No EOL
966 B
YAML
31 lines
No EOL
966 B
YAML
# Note here that with < 3 workers, APEX can behave a little unstably
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# due to the (static) per-worker-epsilon distribution, which also makes
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# evaluation w/o evaluation worker set harder.
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# For an epsilon-free/greedy evaluation, use:
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# evaluation_interval: 1
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# evaluation_config:
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# explore: False
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cartpole-apex-dqn-training-itr:
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env: CartPole-v0
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run: APEX
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stop:
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episode_reward_mean: 150.0
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timesteps_total: 250000
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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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# Make this work with only 5 CPUs and 0 GPUs:
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num_workers: 3
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optimizer:
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num_replay_buffer_shards: 2
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replay_buffer_config:
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type: MultiAgentPrioritizedReplayBuffer
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capacity: 20000
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learning_starts: 1000
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num_gpus: 0
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min_time_s_per_iteration: 5
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target_network_update_freq: 500
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min_sample_timesteps_per_iteration: 1000
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training_intensity: 4 |