Fitted Q-Iteration via Max-Plus-Linear Approximation

Y. Liu; M. A. S. Kolarijani

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arXiv · 2409.08422

Fitted Q-Iteration via Max-Plus-Linear Approximation

Y. Liu·M. A. S. Kolarijani

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Abstract

In this study, we consider the application of max-plus-linear approximators for Q-function in offline reinforcement learning of discounted Markov decision processes. In particular, we incorporate these approximators to propose novel fitted Q-iteration (FQI) algorithms with provable convergence. Exploiting the compatibility of the Bellman operator with max-plus operations, we show that the max-plus-linear regression within each iteration of the proposed FQI algorithm reduces to simple max-plus matrix-vector multiplications. We also consider the variational implementation of the proposed algorithm which leads to a per-iteration complexity that is independent of the number of samples.

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BibTeX RIS

Y. Liu, M. A. S. Kolarijani. 2024-09-12. Fitted Q-Iteration via Max-Plus-Linear Approximation. https://arxiv.org/abs/2409.08422

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