• Corpus ID: 219573558

Online Learning in Iterated Prisoner's Dilemma to Mimic Human Behavior

@article{Lin2020OnlineLI,
  title={Online Learning in Iterated Prisoner's Dilemma to Mimic Human Behavior},
  author={Baihan Lin and Djallel Bouneffouf and Guillermo A. Cecchi},
  journal={ArXiv},
  year={2020},
  volume={abs/2006.06580}
}
Prisoner's Dilemma mainly treat the choice to cooperate or defect as an atomic action. We propose to study online learning algorithm behavior in the Iterated Prisoner's Dilemma (IPD) game, where we explored the full spectrum of reinforcement learning agents: multi-armed bandits, contextual bandits and reinforcement learning. We have evaluate them based on a tournament of iterated prisoner's dilemma where multiple agents can compete in a sequential fashion. This allows us to analyze the dynamics… 
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