@inproceedings{efroni2022provable,
author = {Efroni, Yonathan and Jin, Chi and Krishnamurthy, Akshay and , Sobhan Miryoosefi},
title = {Provable Reinforcement Learning with a Short-Term Memory},
booktitle = {ICML 2022},
year = {2022},
month = {July},
abstract = {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.},
url = {http://approjects.co.za/?big=en-us/research/publication/provable-reinforcement-learning-with-a-short-term-memory/},
}