DARLR uses a selector agent to pick similar and diverse reference users, then averages their predicted rewards to dynamically refine the reward and uncertainty used to train a recommender policy in offline RL.
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DARLR: Dual-Agent Offline Reinforcement Learning for Recommender Systems with Dynamic Reward
DARLR uses a selector agent to pick similar and diverse reference users, then averages their predicted rewards to dynamically refine the reward and uncertainty used to train a recommender policy in offline RL.