2025
Pretraining a Shared Q-Network for Data-Efficient Offline Reinforcement Learning
NeurIPS 2025poster
Offline reinforcement learning (RL) aims to learn a policy from a fixed dataset without additional environment interaction. However, effective offline policy learning often requires a large and diverse dataset to mitigate epistemic uncertainty. Collecting such data demands substantial online interac…