Qualitative Transfer for Reinforcement Learning with Continuous State and Action Spaces

In this work we present a novel approach to transfer knowledge between reinforcement learning tasks with continuous states and actions, where the transition and policy functions are approximated by Gaussian Processes (GPs). The novelty in the proposed approach lies in the idea of transferring qualitative knowledge between tasks, we do so by using the GPs’ hyper-parameters used to represent the state transition function in the source task, which represents qualitative knowledge about the type of transition function that the target task might have. We show that the proposed technique constrains the search space, which accelerates the learning process. We performed experiments varying the relevance of transferring the hyper-parameters from the source task into the target task and show, in general, a clear improvement in the overall performance of the system when compared to a state of the art reinforcement learning algorithm for continuous state and action spaces without transfer.