Cross Domain Policy Transfer

Cross-domain policy transfer aims to adapt policies learned in one environment (source domain) to perform effectively in a different, but related, environment (target domain). Current research focuses on methods that either align representations across domains, leveraging techniques like behavioral cloning and maximum mean discrepancy, or directly edit trajectories from the source domain to better match the target domain's distribution using diffusion models. This research is crucial for improving the robustness and efficiency of reinforcement learning, particularly in robotics and other applications where collecting data in the target domain is expensive or dangerous, enabling faster deployment and wider applicability of learned policies.