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40 lines
1.4 KiB
YAML
40 lines
1.4 KiB
YAML
long-term-satisfaction-recsim-env-slateq:
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env: ray.rllib.examples.env.recsim_recommender_system_envs.LongTermSatisfactionRecSimEnv
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run: SlateQ
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stop:
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episode_reward_mean: 2000.0
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timesteps_total: 100000
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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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# RLlib/RecSim wrapper specific settings:
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env_config:
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config:
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# Each step, sample 100 documents using the env-internal
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# document sampler model (a logic that creates n documents to select
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# the slate from).
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resample_documents: true
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num_candidates: 25
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# How many documents to recommend (out of `num_candidates`) each
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# timestep?
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slate_size: 2
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# Should the action space be purely Discrete? Useful for algos that
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# don't support MultiDiscrete (e.g. DQN).
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# SlateQ handles MultiDiscrete action spaces.
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convert_to_discrete_action_space: false
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seed: 42
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hiddens: [256, 256]
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num_workers: 0
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num_gpus: 0
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lr_choice_model: 0.003
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lr_q_model: 0.003
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rollout_fragment_length: 4
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#batch_mode: complete_episodes
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exploration_config:
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epsilon_timesteps: 50000
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target_network_update_freq: 800
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