Reinforcement Learning Produces Dominant Strategies for the Iterated Prisoner's Dilemma
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). First posted on arXiv 19 Jul 2017.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2022: 2; 2023: 2; 2024: 2; 2025: 1.
7 papers.
- 2022Model-Free Opponent Shapingcited by 43
- 2022COLA: Consistent Learning with Opponent-Learning Awarenesscited by 38
- 2023Scaling Opponent Shaping to High Dimensional Gamescited by 12
- 2023Modeling Moral Choices in Social Dilemmas with Multi-Agent Reinforcement Learningcited by 10
- 2025Opponent Shaping in LLM Agentscited by 2
- 2024Mathematics of multi-agent learning systems at the interface of game theory and artificial intelligencecited by 1
- 2024Preference-based opponent shaping in differentiable gamescited by 0
- 2025Opponent Shaping in LLM Agents9 Oct 2025
- 2024Preference-based opponent shaping in differentiable games4 Dec 2024
- 2024Mathematics of multi-agent learning systems at the interface of game theory and artificial intelligence9 Mar 2024
- 2023Scaling Opponent Shaping to High Dimensional Games19 Dec 2023
- 2023Modeling Moral Choices in Social Dilemmas with Multi-Agent Reinforcement Learning20 Jan 2023
- 2022Model-Free Opponent Shaping3 May 2022
- 2022COLA: Consistent Learning with Opponent-Learning Awareness8 Mar 2022