Provably Efficient Reward Transfer in Reinforcement Learning with Discrete Markov Decision Processes

In this paper, we propose a new solution to reward adaptation (RA) in reinforcement learning, where the agent adapts to a target reward function based on one or more existing source behaviors learned a priori under the same domain dynamics but different reward functions. While learning the target be