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108 lines
3.3 KiB
Python
108 lines
3.3 KiB
Python
"""Examples for RecSim envs ready to be used by RLlib Trainers
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RecSim is a configurable recommender systems simulation platform.
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Source: https://github.com/google-research/recsim
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"""
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from recsim import choice_model
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from recsim.environments import (
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long_term_satisfaction as lts,
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interest_evolution as iev,
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interest_exploration as iex,
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)
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from ray.rllib.env.wrappers.recsim import make_recsim_env
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from ray.tune import register_env
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# Some built-in RecSim envs to test with.
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# ---------------------------------------
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# Long-term satisfaction env: User has to pick from items that are either
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# a) unhealthy, but taste good, or b) healthy, but have bad taste.
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# Best strategy is to pick a mix of both to ensure long-term
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# engagement.
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def lts_user_model_creator(env_ctx):
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return lts.LTSUserModel(
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env_ctx["slate_size"],
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user_state_ctor=lts.LTSUserState,
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response_model_ctor=lts.LTSResponse,
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)
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def lts_document_sampler_creator(env_ctx):
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return lts.LTSDocumentSampler()
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LongTermSatisfactionRecSimEnv = make_recsim_env(
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recsim_user_model_creator=lts_user_model_creator,
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recsim_document_sampler_creator=lts_document_sampler_creator,
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reward_aggregator=lts.clicked_engagement_reward,
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)
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# Interest exploration env: Models the problem of active exploration
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# of user interests. It is meant to illustrate popularity bias in
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# recommender systems, where myopic maximization of engagement leads
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# to bias towards documents that have wider appeal,
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# whereas niche user interests remain unexplored.
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def iex_user_model_creator(env_ctx):
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return iex.IEUserModel(
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env_ctx["slate_size"],
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user_state_ctor=iex.IEUserState,
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response_model_ctor=iex.IEResponse,
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seed=env_ctx["seed"],
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)
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def iex_document_sampler_creator(env_ctx):
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return iex.IETopicDocumentSampler(seed=env_ctx["seed"])
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InterestExplorationRecSimEnv = make_recsim_env(
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recsim_user_model_creator=iex_user_model_creator,
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recsim_document_sampler_creator=iex_document_sampler_creator,
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reward_aggregator=iex.total_clicks_reward,
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)
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# Interest evolution env: See https://github.com/google-research/recsim
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# for more information.
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def iev_user_model_creator(env_ctx):
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return iev.IEvUserModel(
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env_ctx["slate_size"],
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choice_model_ctor=choice_model.MultinomialProportionalChoiceModel,
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response_model_ctor=iev.IEvResponse,
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user_state_ctor=iev.IEvUserState,
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seed=env_ctx["seed"],
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)
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# Extend IEvVideo to fix a bug caused by None cluster_ids.
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class SingleClusterIEvVideo(iev.IEvVideo):
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def __init__(self, doc_id, features, video_length=None, quality=None):
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super(SingleClusterIEvVideo, self).__init__(
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doc_id=doc_id,
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features=features,
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cluster_id=0, # single cluster.
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video_length=video_length,
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quality=quality,
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)
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def iev_document_sampler_creator(env_ctx):
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return iev.UtilityModelVideoSampler(doc_ctor=iev.IEvVideo, seed=env_ctx["seed"])
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InterestEvolutionRecSimEnv = make_recsim_env(
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recsim_user_model_creator=iev_user_model_creator,
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recsim_document_sampler_creator=iev_document_sampler_creator,
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reward_aggregator=iev.clicked_watchtime_reward,
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)
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# Backward compatibility.
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register_env(
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name="RecSim-v1", env_creator=lambda env_ctx: InterestEvolutionRecSimEnv(env_ctx)
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)
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