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Provably Efficient Representation Learning with Tractable Planning in Low-Rank POMDP
Jiacheng Guo · Zihao Li · Huazheng Wang · Mengdi Wang · Zhuoran Yang · Xuezhou Zhang

Wed Jul 26 02:00 PM -- 03:30 PM (PDT) @ Exhibit Hall 1 #430
In this paper, we study representation learning in partially observable Markov Decision Processes (POMDPs), where the agent learns a decoder function that maps a series of high-dimensional raw observations to a compact representation and uses it for more efficient exploration and planning. We focus our attention on the sub-classes of *$\gamma$-observable* and *decodable POMDPs*, for which it has been shown that statistically tractable learning is possible, but there has not been any computationally efficient algorithm. We first present an algorithm for decodable PMMDPs that combines maximum likelihood estimation (MLE) and optimism in the face of uncertainty (OFU) to perform representation learning and achieve efficient sample complexity, while only calling supervised learning computational oracles. We then show how to adapt this algorithm to also work in the broader class of $\gamma$-observable POMDPs.

Author Information

Jiacheng Guo (Fudan University)
Zihao Li (Princeton University)
Huazheng Wang (Oregon State University)
Mengdi Wang (Princeton University)
Zhuoran Yang (Yale University)
Xuezhou Zhang (Princeton University)

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