1234567891011121314151617181920212223242526272829 |
- interest-evolution-recsim-env-bandit-linucb:
- env: ray.rllib.examples.env.recommender_system_envs_with_recsim.InterestEvolutionRecSimEnv
- run: BanditLinUCB
- stop:
- sampler_results/episode_reward_mean: 180.0
- timesteps_total: 50000
- config:
- framework: torch
- # RLlib/RecSim wrapper specific settings:
- env_config:
- # Env class specified above takes one `config` arg in its c'tor:
- config:
- # Each step, sample `num_candidates` documents using the env-internal
- # document sampler model (a logic that creates n documents to select
- # the slate from).
- resample_documents: true
- num_candidates: 100
- # How many documents to recommend (out of `num_candidates`) each
- # timestep?
- slate_size: 2
- # Should the action space be purely Discrete? Useful for algos that
- # don't support MultiDiscrete (e.g. DQN or Bandits).
- # SlateQ handles MultiDiscrete action spaces.
- convert_to_discrete_action_space: true
- wrap_for_bandits: true
- seed: 0
- metrics_num_episodes_for_smoothing: 500
|