Symmetric equilibrium of multi-agent reinforcement learning in repeated prisoner’s dilemma

Yuki Usui and Masahiko Ueda

Applied Mathematics and Computation, 2021, vol. 409, issue C

Abstract: We investigate the repeated prisoner’s dilemma game where both players alternately use reinforcement learning to obtain their optimal memory-one strategies. We theoretically solve the simultaneous Bellman optimality equations of reinforcement learning. We find that the Win-stay Lose-shift strategy, the Grim strategy, and the strategy which always defects can form symmetric equilibrium of the mutual reinforcement learning process amongst all deterministic memory-one strategies.

Keywords: Repeated prisoner’s dilemma game; Reinforcement learning (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:409:y:2021:i:c:s0096300321004598

DOI: 10.1016/j.amc.2021.126370

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