Online KL-Regularized Reinforcement Learning with Function Approximation under Misspecification
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Computer Science > Machine Learning
Title:Online KL-Regularized Reinforcement Learning with Function Approximation under Misspecification
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 standard realizable KL-regularized setting as a special case.
| Comments: | Accepted by RLC 2026 |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2606.06053 [cs.LG] |
| (or arXiv:2606.06053v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2606.06053
arXiv-issued DOI via DataCite (pending registration)
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