Using Reward Machines for HighLevel Task Specification and Decomposition in Reinforcement Learning
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Proceedings of the 35th International Conference on Machine Learning, PMLR 80:21072116, 2018.
Abstract
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 QLearning for Reward Machines (QRM), an algorithm which appropriately decomposes the reward machine and uses offpolicy qlearning to simultaneously learn subpolicies for the different components. QRM is guaranteed to converge to an optimal policy in the tabular case, in contrast to Hierarchical Reinforcement Learning methods which might converge to suboptimal policies. We demonstrate this behavior experimentally in two discrete domains. We also show how function approximation methods like neural networks can be incorporated into QRM, and that doing so can find better policies more quickly than hierarchical methods in a domain with a continuous state space.
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