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arXiv · 2505.12350

Multi-CALF: A Policy Combination Approach with Statistical Guarantees

Georgiy Malaniya·Anton Bolychev·Grigory Yaremenko·Anastasia Krasnaya·Pavel Osinenko
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Abstract

We introduce Multi-CALF, an algorithm that intelligently combines reinforcement learning policies based on their relative value improvements. Our approach integrates a standard RL policy with a theoretically-backed alternative policy, inheriting formal stability guarantees while often achieving better performance than either policy individually. We prove that our combined policy converges to a specified goal set with known probability and provide precise bounds on maximum deviation and convergence time. Empirical validation on control tasks demonstrates enhanced performance while maintaining stability guarantees.

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Georgiy Malaniya, Anton Bolychev, Grigory Yaremenko, Anastasia Krasnaya, Pavel Osinenko. 2025-05-18. Multi-CALF: A Policy Combination Approach with Statistical Guarantees. https://arxiv.org/abs/2505.12350

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