arXiv:2606.07592v1 Announce Type: new Abstract: Offline reinforcement learning requires careful conservatism to mitigate distribution shift, yet most existing methods apply a fixed penalty uniformly across all states regardless of local data coverage. We present UNIQ (Uncertainty-Informed Quantile), an offline RL method that introduces state-adaptive conservatism through conformally calibrated uncertainty estimation. Built on the Implicit Q-Learning (IQL) backbone, UNIQ trains a multi-expectile value ensemble, computes distribution-free uncertainty estimates using split conformal prediction, a
This research addresses a fundamental challenge in offline reinforcement learning (RL) – ensuring reliability and safety when training from fixed datasets, a crucial step for deploying RL in real-world applications.
Adaptive conservatism and robust uncertainty estimation are critical for deploying AI safely and effectively in complex, safety-sensitive domains like robotics or autonomous systems, mitigating risks from distributional shifts.
This method introduces a more dynamic and context-aware approach to conservatism in offline RL, potentially leading to more reliable and generalizable AI agent behaviors compared to fixed penalty systems.
- · AI/ML researchers
- · Robotics developers
- · Autonomous system manufacturers
- · Industries relying on offline RL for simulation and training
- · Methods using fixed, non-adaptive conservatism in offline RL
Improved reliability and safety metrics for AI agents trained with offline reinforcement learning.
Faster and safer deployment of AI agents in real-world applications where data collection is expensive or risky.
Accelerated development of complex autonomous AI systems benefiting from more robust decision-making capabilities.
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Read at arXiv cs.LG