Exclusively Penalized Q-learning for Offline Reinforcement Learning

Apply value penalties selectively to reduce estimation bias in offline reinforcement learning.

EPQ adapts value penalties to states where a learned policy lacks sufficient support in the offline data. Combined with dataset prioritization, this reduces unnecessary pessimism while addressing distribution shift, with evaluations on D4RL control tasks.