Policy Transfer

Policy transfer in robotics and reinforcement learning aims to leverage knowledge gained from one task or robot to accelerate learning in a new, related context, reducing the need for extensive retraining. Current research focuses on developing algorithms that enable efficient transfer across diverse robots with varying morphologies and sensors, employing techniques like evolutionary methods, generative models (e.g., GANs), and successor features to bridge domain gaps. These advancements are significant because they promise to improve the efficiency and scalability of training robots for complex tasks, particularly in scenarios where data collection is expensive or dangerous.