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https://github.com/vale981/ray
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dd70720578
* bulk rename * deprecation warn * update doc * update fig * line length * rename * make pytest comptaible * fix test * fi sys * rename * wip * fix more * lint * update svg * comments * lint * fix use of batch steps
29 lines
927 B
YAML
29 lines
927 B
YAML
# This can reach 18-19 reward in ~5-7 minutes on a Titan XP GPU
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# with 32 workers and 8 envs per worker. IMPALA, when ran with
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# similar configurations, solved Pong in 10-12 minutes.
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# APPO can also solve Pong in 2.5 million timesteps, which is
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# 2x more efficient than that of IMPALA.
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pong-appo:
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env: PongNoFrameskip-v4
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run: APPO
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stop:
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episode_reward_mean: 18.0
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timesteps_total: 5000000
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config:
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vtrace: True
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use_kl_loss: False
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rollout_fragment_length: 50
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train_batch_size: 750
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num_workers: 32
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broadcast_interval: 1
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max_sample_requests_in_flight_per_worker: 1
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num_data_loader_buffers: 1
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num_envs_per_worker: 8
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minibatch_buffer_size: 4
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num_sgd_iter: 2
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vf_loss_coeff: 1.0
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clip_param: 0.3
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num_gpus: 1
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grad_clip: 10
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model:
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dim: 42
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