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Reinforcement learning produces dominant strategies for the Iterated Prisoner’s Dilemma

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Listed:
  • Marc Harper
  • Vincent Knight
  • Martin Jones
  • Georgios Koutsovoulos
  • Nikoleta E Glynatsi
  • Owen Campbell

Abstract

We present tournament results and several powerful strategies for the Iterated Prisoner’s Dilemma created using reinforcement learning techniques (evolutionary and particle swarm algorithms). These strategies are trained to perform well against a corpus of over 170 distinct opponents, including many well-known and classic strategies. All the trained strategies win standard tournaments against the total collection of other opponents. The trained strategies and one particular human made designed strategy are the top performers in noisy tournaments also.

Suggested Citation

  • Marc Harper & Vincent Knight & Martin Jones & Georgios Koutsovoulos & Nikoleta E Glynatsi & Owen Campbell, 2017. "Reinforcement learning produces dominant strategies for the Iterated Prisoner’s Dilemma," PLOS ONE, Public Library of Science, vol. 12(12), pages 1-33, December.
    • Handle: RePEc:plo:pone00:0188046
    DOI: 10.1371/journal.pone.0188046
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    References listed on IDEAS

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    Cited by:

    1. Ding, Zhen-Wei & Zheng, Guo-Zhong & Cai, Chao-Ran & Cai, Wei-Ran & Chen, Li & Zhang, Ji-Qiang & Wang, Xu-Ming, 2023. "Emergence of cooperation in two-agent repeated games with reinforcement learning," Chaos, Solitons & Fractals, Elsevier, vol. 175(P1).
    2. Molnar, Grant & Hammond, Caroline & Fu, Feng, 2023. "Reactive means in the iterated Prisoner’s dilemma," Applied Mathematics and Computation, Elsevier, vol. 458(C).
    3. Ueda, Masahiko, 2023. "Memory-two strategies forming symmetric mutual reinforcement learning equilibrium in repeated prisoners’ dilemma game," Applied Mathematics and Computation, Elsevier, vol. 444(C).
    4. Vincent Knight & Marc Harper & Nikoleta E Glynatsi & Owen Campbell, 2018. "Evolution reinforces cooperation with the emergence of self-recognition mechanisms: An empirical study of strategies in the Moran process for the iterated prisoner’s dilemma," PLOS ONE, Public Library of Science, vol. 13(10), pages 1-33, October.
    5. Usui, Yuki & Ueda, Masahiko, 2021. "Symmetric equilibrium of multi-agent reinforcement learning in repeated prisoner’s dilemma," Applied Mathematics and Computation, Elsevier, vol. 409(C).

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