In this paper, we present a new approach to transfer in Reinforcement Learning (RL) for cross-domain tasks. Unlike, available transfer approaches, where target task learning is accelerated through initialized learning from source, we propose to adapt and reuse the optimal source policy directly in the related domains. We show the optimal policy from a related source task can be near optimal in target domain provided an adaptive policy accounts for the model error between target and the projected source. A significant advantage of the proposed policy augmentation is in generalizing the policies across related domains without having to re-learn the new tasks. We demonstrate that, this architecture leads to better sample efficiency in the transfer, reducing sample complexity of target task learning to target apprentice learning.
In REF , an adaptive policy is introduced to compensate the projection error between the source and target agents.
Cross-Domain Transfer in Reinforcement Learning Using Target Apprentice
2018 IEEE International Conference on Robotics and Automation (ICRA)
2018 IEEE International Conference on Robotics and Automation (ICRA)
Engineering Computer Science Mathematics