Beyond Policy Support: Interaction Constrained Offline Reinforcement Learning for Autonomous Driving
Overview
New research introduces ICDP, an offline reinforcement learning framework for autonomous driving that addresses interaction distribution shift by decomposing support degradation into ego and interaction components, improving performance in critical scenarios through evaluations on nuPlan, Interplan, and real-world truck experiments.
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- arXiv Robotics — research abstractsBeyond Policy Support: Interaction Constrained Offline Reinforcement Learning for Autonomous Driving
This research introduces Interaction-Constrained Drive Policy (ICDP), an offline reinforcement learning framework designed to address interaction distribution shift in autonomous driving. By decomposing joint-support degradation into ego-support and interaction-support components, ICDP suppresses high-value yet interaction-unsupported trajectory selections, improving performance in interaction-critical driving scenarios through evaluations on nuPlan, Interplan, and real-world truck experiments.
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