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
The continuous drive for more robust and reliable AI systems fuels research into advanced reinforcement learning techniques that can handle real-world complexities like model misspecification.
Improving the theoretical foundations and practical applicability of reinforcement learning, especially under misspecification, is crucial for developing AI agents capable of operating effectively in uncertain and complex environments.
This research provides new theoretical guarantees for reinforcement learning algorithms in settings where ideal model assumptions do not hold, potentially enabling more resilient general function approximation.
- · AI researchers and developers
- · Robotics
- · Autonomous systems
- · Systems relying on naive RL assumptions
Improved theoretical understanding of RL under model inaccuracies facilitates the deployment of more reliable AI.
This could accelerate the development of sophisticated autonomous agents that are less prone to failure when faced with unexpected real-world conditions.
Enhanced AI agent robustness might lead to broader adoption of AI in safety-critical applications, potentially impacting overall economic productivity and specialized labor markets.
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