Q-learning as a monotone scheme

Lingyi Yang
5/30/2024
cs.LG

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Abstract

Stability issues with reinforcement learning methods persist. To better understand some of these stability and convergence issues involving deep reinforcement learning methods, we examine a simple linear quadratic example. We interpret the convergence criterion of exact Q-learning in the sense of a monotone scheme and discuss consequences of function approximation on monotonicity properties.

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Cite this paper

@article{yang2024qlearning,
  title  = {Q-learning as a monotone scheme},
  author = {Lingyi Yang},
  year   = {2024},
  eprint = {2405.20538},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2405.20538v1}
}

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