Composite Flow Matching for Reinforcement Learning with Shifted-Dynamics Data

The paper introduces CompFlow, a novel method for reinforcement learning that models target dynamics using flow matching and optimal transport principles, enabling better generalization and more accurate estimation of the dynamics gap via Wasserstein distance. This approach facilitates an active exploration strategy that reduces performance gaps in environments with shifted dynamics, outperforming existing baselines across various RL benchmarks.