mirror of
https://github.com/vale981/ray
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33 lines
1.2 KiB
YAML
33 lines
1.2 KiB
YAML
# Basically the same as atari-ppo, but adapted for DDPPO. Note that DDPPO
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# isn't actually any more efficient on Atari, since the network size is
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# relatively small and the env doesn't require a GPU.
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atari-ddppo:
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env:
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grid_search:
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- BreakoutNoFrameskip-v4
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run: DDPPO
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config:
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# DDPPO only supports PyTorch so far.
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framework: torch
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# Worker config: 10 workers, each of which requires a GPU.
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num_workers: 10
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num_gpus_per_worker: 1
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# Each worker will sample 100 * 5 envs per worker steps = 500 steps
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# per optimization round. This is 5000 steps summed across workers.
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rollout_fragment_length: 100
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num_envs_per_worker: 5
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# Each worker will take a minibatch of 50. There are 10 workers total,
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# so the effective minibatch size will be 500.
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sgd_minibatch_size: 50
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num_sgd_iter: 10
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# Params from standard PPO Atari config:
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lambda: 0.95
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kl_coeff: 0.5
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clip_rewards: True
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clip_param: 0.1
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vf_clip_param: 10.0
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entropy_coeff: 0.01
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batch_mode: truncate_episodes
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observation_filter: NoFilter
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model:
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vf_share_layers: true
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