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41 lines
1.5 KiB
YAML
41 lines
1.5 KiB
YAML
slateq-interest-evolution-recsim-env:
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env: ray.rllib.examples.env.recommender_system_envs_with_recsim.InterestEvolutionRecSimEnv
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run: SlateQ
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pass_criteria:
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episode_reward_mean: 162.0
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timesteps_total: 300000
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stop:
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time_total_s: 7200
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config:
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# increase num sampling workers for faster sampling.
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num_workers: 12
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# RLlib/RecSim wrapper specific settings:
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env_config:
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# Env class specified above takes one `config` arg in its c'tor:
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config:
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# Each step, sample `num_candidates` 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: 50
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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 or Bandits).
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# SlateQ handles MultiDiscrete action spaces.
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convert_to_discrete_action_space: false
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seed: 0
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num_gpus: 1
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exploration_config:
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warmup_timesteps: 20000
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epsilon_timesteps: 70000
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replay_buffer_config:
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capacity: 500000
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lr: 0.00025
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metrics_num_episodes_for_smoothing: 200
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