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Reinforcement Learning In Markovian Evolutionary Games

Author

Listed:
  • V. S. BORKAR

    (School of Technology and Computer Science, Tata Institute of Fundamental Research, Homi Bhabha Road, Mumbai 400005, India)

Abstract

A population of agents plays a stochastic dynamic game wherein there is an underlying state process with a Markovian dynamics that also affects their costs. A learning mechanism is proposed which takes into account intertemporal effects and incorporates an explicit process of expectation formation. The agents use this scheme to update their mixed strategies incrementally. The asymptotic behavior of this scheme is captured by an associated ordinary differential equation. Both the formulation and the analysis of the scheme draw upon the theory of reinforcement learning in artificial intelligence.

Suggested Citation

  • V. S. Borkar, 2002. "Reinforcement Learning In Markovian Evolutionary Games," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 5(01), pages 55-72.
    • Handle: RePEc:wsi:acsxxx:v:05:y:2002:i:01:n:s0219525902000535
    DOI: 10.1142/S0219525902000535
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    Cited by:

    1. Alfredo Garcia & Enrique Campos-Nañez & James Reitzes, 2005. "Dynamic Pricing and Learning in Electricity Markets," Operations Research, INFORMS, vol. 53(2), pages 231-241, April.
    2. Leslie, David S. & Perkins, Steven & Xu, Zibo, 2020. "Best-response dynamics in zero-sum stochastic games," Journal of Economic Theory, Elsevier, vol. 189(C).
    3. Geng, Yini & Liu, Yifan & Lu, Yikang & Shen, Chen & Shi, Lei, 2022. "Reinforcement learning explains various conditional cooperation," Applied Mathematics and Computation, Elsevier, vol. 427(C).

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