Recurrent Natural Policy Gradient for POMDPs

Published: 17 Jun 2024, Last Modified: 23 Jul 2024FoRLaC PosterEveryoneRevisionsBibTeXCC BY 4.0
Abstract: In this paper, we study a natural policy gradient method based on recurrent neural networks (RNNs) for partially-observable Markov decision processes, whereby RNNs are used for policy parameterization and policy evaluation to address curse of dimensionality in non-Markovian reinforcement learning. We present finite-time and finite-width analyses for both the critic (recurrent temporal difference learning), and correspondingly-operated recurrent natural policy gradient method in the near-initialization regime. Our analysis demonstrates the efficiency of RNNs for problems with short-term memory with explicit bounds on the required network widths and sample complexity, and points out the challenges in the case of long-term dependencies.
Format: Long format (up to 8 pages + refs, appendix)
Publication Status: No
Submission Number: 20
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