Multi-CALF: A Policy Combination Approach with Statistical Guarantees

-- We introduce Multi-CALF, an algorithm that intelligently combines reinforcement learning policies based on their relative value improvements. 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. I. INTRODUCTION Reinforcement learning (RL) has demonstrated remarkable effectiveness for solving complex control problems across diverse domains, from robotic manipulation [1], [2], [3], [4] to games [5], [6], [7]. Policy synthesis [12], [13], which combines policies with complementary strengths, has emerged as a promising approach to address these limitations.