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arXiv · 2110.13855

Average-Reward Learning and Planning with Options

Yi Wan·Abhishek Naik·Richard S. Sutton
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Abstract

We extend the options framework for temporal abstraction in reinforcement learning from discounted Markov decision processes (MDPs) to average-reward MDPs. Our contributions include general convergent off-policy inter-option learning algorithms, intra-option algorithms for learning values and models, as well as sample-based planning variants of our learning algorithms. Our algorithms and convergence proofs extend those recently developed by Wan, Naik, and Sutton. We also extend the notion of option-interrupting behavior from the discounted to the average-reward formulation. We show the efficacy of the proposed algorithms with experiments on a continuing version of the Four-Room domain.

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Yi Wan, Abhishek Naik, Richard S. Sutton. 2021-10-26. Average-Reward Learning and Planning with Options. https://arxiv.org/abs/2110.13855

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