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.
View on arXiv@article{malaniya2025_2505.12350,
title={ Multi-CALF: A Policy Combination Approach with Statistical Guarantees },
author={ Georgiy Malaniya and Anton Bolychev and Grigory Yaremenko and Anastasia Krasnaya and Pavel Osinenko },
journal={arXiv preprint arXiv:2505.12350},
year={ 2025 }
}