Reinforcement learning produces dominant strategies for the Iterated Prisoner's Dilemma
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...
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| Veröffentlicht in: | PloS one 2017-12, Vol.12 (12), p.e0188046-e0188046 |
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| creator | Harper, Marc Knight, Vincent Jones, Martin Koutsovoulos, Georgios Glynatsi, Nikoleta E Campbell, Owen |
| description | 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. |
| doi_str_mv | 10.1371/journal.pone.0188046 |
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| subjects | Algorithms Biology and Life Sciences Computer and Information Sciences Cooperation Environmental Sciences Evolution Evolutionary algorithms Game Theory Humans Learning Library collections Life Sciences Machine learning Physical Sciences Prisoner Dilemma Reinforcement Reinforcement learning (Machine learning) Research and Analysis Methods Researchers Tournaments & championships |
| title | Reinforcement learning produces dominant strategies for the Iterated Prisoner's Dilemma |
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