Poster

Average-Reward Learning and Planning with Options

Yi Wan · Abhishek Naik · Rich Sutton

Keywords: [ Reinforcement Learning and Planning ]

[ Abstract ]
Thu 9 Dec 8:30 a.m. PST — 10 a.m. PST

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 behaviour 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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