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On the Distributional Convergence of Temporal Difference Learning

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Machine Learning and Knowledge Discovery in Databases: Research Track (ECML PKDD 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14172))

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Abstract

Temporal Difference (TD) learning is one of the most simple but efficient algorithms for policy evaluation in reinforcement learning. Although the finite-time convergence results of the TD algorithm are abundant now, the distributional convergence has still been blank. This paper shows that TD with constant step size simulates Markov chains converging to some stationary distribution under both i.i.d. and Markov chain observation models. We prove that TD enjoys the geometric distributional convergence rate and show how the step size affects the expectation and covariance of the stationary distribution. All assumptions used in our paper are mild and common in the TD community. Our proved results indicate a tradeoff between the convergence speed and accuracy for TD. Based on our theoretical findings, we explain why the Jacobi preconditioner can accelerate the TD algorithms.

J. Dai–Independent Researcher.

This research is supported by the National Natural Science Foundation of China under the grant (12002382).

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Notes

  1. 1.

    We call it semi-gradient because the \(\textbf{g}^t\) does not follow the stochastic gradient direction of any fixed objective.

  2. 2.

    The i.i.d. observation model happens if the initial state \(s_0\) is drawn from the stationary distribution.

  3. 3.

    In practice, \(\textbf{J}\) is approximated by a Monte Carlo method.

  4. 4.

    The random walk gradient descent can be regarded as a special MCGD because the random walk is a Markov chain process.

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Authors and Affiliations

  1. Changsha, China

    Jie Dai

  2. College of Computer, National University of Defense Technology, Changsha, China

    Xuguang Chen

Authors
  1. Jie Dai
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  2. Xuguang Chen
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Corresponding author

Correspondence to Xuguang Chen .

Editor information

Editors and Affiliations

  1. University of Michigan, Ann Arbor, MI, USA

    Danai Koutra

  2. University of Vienna, Vienna, Austria

    Claudia Plant

  3. Max Planck Institute for Software Systems, Kaiserslautern, Germany

    Manuel Gomez Rodriguez

  4. Politecnico di Torino, Turin, Italy

    Elena Baralis

  5. CENTAI, Turin, Italy

    Francesco Bonchi

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Our paper is devoted to the theoretical aspect of general stochastic algorithm, which does not present any foreseeable societal consequence.

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Dai, J., Chen, X. (2023). On the Distributional Convergence of Temporal Difference Learning. In: Koutra, D., Plant, C., Gomez Rodriguez, M., Baralis, E., Bonchi, F. (eds) Machine Learning and Knowledge Discovery in Databases: Research Track. ECML PKDD 2023. Lecture Notes in Computer Science(), vol 14172. Springer, Cham. https://doi.org/10.1007/978-3-031-43421-1_26

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  • DOI: https://doi.org/10.1007/978-3-031-43421-1_26

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-43420-4

  • Online ISBN: 978-3-031-43421-1

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