Keywords: average reward, options, reinforcement learning
TL;DR: This paper extends learning and planning algorithms within the options framework (Sutton et al. 1999) from discounted MDPs to average-reward MDPs.
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.
Supplementary Material: pdf
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