Provably Efficient Representation Learning with Tractable Planning in Low-Rank POMDP
This addresses the problem of computationally efficient learning in POMDPs for reinforcement learning researchers, though it builds on existing tractability results and is incremental in providing computational efficiency.
The paper tackles representation learning in partially observable Markov Decision Processes (POMDPs) by developing a computationally efficient algorithm for decodable and γ-observable POMDPs, achieving efficient sample complexity using supervised learning oracles.
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 \textit{$γ$-observable} and \textit{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 POMDPs 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 $γ$-observable POMDPs.