Online KL-Regularized Reinforcement Learning with Function Approximation under Misspecification

arXiv:2606.06053v1 Announce Type: new Abstract: We study KL-regularized contextual bandits and episodic reinforcement learning (RL) under general function approximation with model misspecification. Existing guarantees rely on realizability and therefore do not extend to misspecified models, where classical regret bounds may fail. This work introduces KL misspecification formulations for contextual bandits and episodic RL and analyzes regression-based algorithms with Gibbs policy updates. High-probability KL-regret guarantees with explicit misspecification terms are established, recovering the

Source: arXiv cs.LG — read the full report at the original publisher.

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