AI RESEARCH
Escaping Offline Pessimism: Vector-Field Reward Shaping for Safe Frontier Exploration
arXiv CS.LG
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ArXi:2603.18326v1 Announce Type: new While offline reinforcement learning provides reliable policies for real-world deployment, its inherent pessimism severely restricts an agent's ability to explore and collect novel data online. Drawing inspiration from safe reinforcement learning, exploring near the boundary of regions well covered by the offline dataset and reliably modeled by the simulator allows an agent to take manageable risks--venturing into informative but moderate-uncertainty states while remaining close enough to familiar regions for safe recovery.