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Poster
Model-free Reinforcement Learning in Infinite-horizon Average-reward Markov Decision Processes
Chen-Yu Wei · Mehdi Jafarnia · Haipeng Luo · Hiteshi Sharma · Rahul Jain

Wed Jul 15 02:00 PM -- 02:45 PM & Thu Jul 16 03:00 AM -- 03:45 AM (PDT) @
in Poster Session 28 »
Model-free reinforcement learning is known to be memory and computation efficient and more amendable to large scale problems. In this paper, two model-free algorithms are introduced for learning infinite-horizon average-reward Markov Decision Processes (MDPs). The first algorithm reduces the problem to the discounted-reward version and achieves $\mathcal{O}(T^{2/3})$ regret after $T$ steps, under the minimal assumption of weakly communicating MDPs. To our knowledge, this is the first model-free algorithm for general MDPs in this setting. The second algorithm makes use of recent advances in adaptive algorithms for adversarial multi-armed bandits and improves the regret to $\mathcal{O}(\sqrt{T})$, albeit with a stronger ergodic assumption. This result significantly improves over the $\mathcal{O}(T^{3/4})$ regret achieved by the only existing model-free algorithm by Abbasi-Yadkori et al. (2019) for ergodic MDPs in the infinite-horizon average-reward setting.
Keywords: Online Learning / Bandits · Reinforcement Learning Theory · Reinforcement Learning - Theory

Author Information

Chen-Yu Wei (University of Southern California)
Mehdi Jafarnia (University of Southern California)
Haipeng Luo (University of Southern California)
Hiteshi Sharma (University of Southern California)
Rahul Jain (USC)

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