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Found 2 papers, 1 papers with code
Robust Offline Reinforcement Learning with Gradient Penalty and Constraint Relaxation
A promising paradigm for offline reinforcement learning (RL) is to constrain the learned policy to stay close to the dataset behaviors, known as policy constraint offline RL.
Value Penalized Q-Learning for Recommender Systems
To alleviate the action distribution shift problem in extracting RL policy from static trajectories, we propose Value Penalized Q-learning (VPQ), an uncertainty-based offline RL algorithm.
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