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Provable Reinforcement Learning with a Short-Term Memory
Yonathan Efroni · Chi Jin · Akshay Krishnamurthy · Sobhan Miryoosefi

Tue Jul 19 07:45 AM -- 07:50 AM (PDT) @ Hall F

Real-world sequential decision making problems commonly involve partial observability, which requires the agent to maintain a memory of history in order to infer the latent states, plan and make good decisions. Coping with partial observability in general is extremely challenging, as a number of worst-case statistical and computational barriers are known in learning Partially Observable Markov Decision Processes (POMDPs). Motivated by the problem structure in several physical applications, as well as a commonly used technique known as "frame stacking", this paper proposes to study a new subclass of POMDPs, whose latent states can be decoded by the most recent history of a short length m. We establish a set of upper and lower bounds on the sample complexity for learning near-optimal policies for this class of problems in both tabular and rich-observation settings (where the number of observations is enormous). In particular, in the rich-observation setting, we develop new algorithms using a novel "moment matching" approach with a sample complexity that scales exponentially with the short length m rather than the problem horizon, and is independent of the number of observations. Our results show that a short-term memory suffices for reinforcement learning in these environments.

Author Information

Yonathan Efroni (Microsoft Research, New York)
Chi Jin (Princeton University)
Akshay Krishnamurthy (Microsoft Research)
Sobhan Miryoosefi (Princeton University)

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