mirror of
https://github.com/vale981/ray
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51 lines
1.5 KiB
YAML
51 lines
1.5 KiB
YAML
recomm-sys001-slateq:
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env: ray.rllib.examples.env.recommender_system_envs.RecommSys001
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run: SlateQ
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stop:
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#evaluation/episode_reward_mean: 48.0
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timesteps_total: 200000
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config:
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# SlateQ only supported for torch so far.
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framework: torch
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metrics_num_episodes_for_smoothing: 1000
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# Env c'tor kwargs:
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env_config:
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# Number of different categories a doc can have and a user can
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# have a preference for.
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num_categories: 5
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# Number of docs to choose (a slate) from each timestep.
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num_docs_to_select_from: 50
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# Slate size.
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slate_size: 2
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# Re-sample docs each timesteps.
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num_docs_in_db: 1000
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# Re-sample user each episode.
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num_users_in_db: 1000
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# User time budget (determines lengths of episodes).
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user_time_budget: 60.0
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grad_clip: 2.0
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# Larger networks seem to help (large obs/action spaces).
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hiddens: [512, 512]
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# Larger batch sizes seem to help (more stability, even with higher lr).
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train_batch_size: 32
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num_workers: 0
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num_gpus: 0
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lr_choice_model: 0.002
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lr_q_model: 0.002
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target_network_update_freq: 500
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tau: 1.0
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# Evaluation settings.
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evaluation_interval: 1
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evaluation_num_workers: 4
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evaluation_duration: 200
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evaluation_duration_unit: episodes
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evaluation_parallel_to_training: true
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