ArticlePloS one2017
Reinforcement learning produces dominant strategies for the Iterated Prisoner's Dilemma.
Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- On the optimal integration of intelligent agents into network systems to steer cooperation.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- A multi-agent reinforcement learning framework for exploring dominant strategies in iterated and evolutionary games.Nature communications · 2025Article
- Conditional cooperation with longer memory.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Properties of winning Iterated Prisoner's Dilemma strategies.PLoS computational biology · 2024Article
- Recognising and evaluating the effectiveness of extortion in the Iterated Prisoner's Dilemma.PloS one · 2024Article
- Outlearning extortioners: unbending strategies can foster reciprocal fairness and cooperation.PNAS nexus · 2023Article
- Direct reciprocity between individuals that use different strategy spaces.PLoS computational biology · 2022Article
- Nash equilibria in human sensorimotor interactions explained by Q-learning with intrinsic costs.Scientific reports · 2021Article
- Using a theory of mind to find best responses to memory-one strategies.Scientific reports · 2020Article
- Cocaine use disorder in females is associated with altered social decision-making: a study with the prisoner's dilemma and the ultimatum game.BMC psychiatry · 2019Article
- Multi-agent reinforcement learning with approximate model learning for competitive games.PloS one · 2019Article
- No Strategy Can Win in the Repeated Prisoner's Dilemma: Linking Game Theory and Computer Simulations.Frontiers in robotics and AI · 2018Article
- Article
- Reinforcement learning produces dominant strategies for the Iterated Prisoner's Dilemma.PloS one · 2017Article
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Authors and funding
6 authors.
Funding
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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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.