Policy Cloning

Policy cloning, a core method in imitation learning, aims to replicate an expert's behavior by training a policy to mimic its actions from observed data. Current research emphasizes improving the robustness and efficiency of policy cloning, focusing on addressing issues like compounding errors, generalization to unseen environments, and data scarcity through techniques such as adversarial training, statistical performance bounds, and data augmentation. These advancements are crucial for deploying reliable and safe imitation learning in real-world applications, particularly in robotics and control systems where high-stakes decisions necessitate trustworthy and generalizable policies.