Using Reward Machines for High-Level Task Specification and Decomposition in Reinforcement Learning

In this paper we propose Reward Machines {—} a type of finite state machine that supports the specification of reward functions while exposing reward function structure to the learner and supporting decomposition. We then present Q-Learning for Reward Machines (QRM), an algorithm which appropriately decomposes the reward machine and uses off-policy q-learning to simultaneously learn subpolicies for the different components... (read more)

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METHOD TYPE
Q-Learning
Off-Policy TD Control