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Dilution, Diffusion and Symbiosis in Spatial Prisoner's Dilemma with Reinforcement Learning

arXiv 2507.02211 ↗First posted 3 Jul 2025cs.AI cs.NE physics.comp-phcited by 0

On the map: Deep reinforcement learning methods → RL for games and combinatorial optimization

Abstract (arXiv)

Recent studies in the spatial prisoner's dilemma games with reinforcement learning have shown that static agents can learn to cooperate through a diverse sort of mechanisms, including noise injection, different types of learning algorithms and neighbours' payoff knowledge. In this work, using an independent multi-agent Q-learning algorithm, we study the effects of dilution and mobility in the spatial version of the prisoner's dilemma. Within this setting, different possible actions for the algorithm are defined, connecting with previous results on the classical, non-reinforcement learning spatial prisoner's dilemma, showcasing the versatility of the algorithm in modeling different game-theoretical scenarios and the benchmarking potential of this approach. As a result, a range of effects is observed, including evidence that games with fixed update rules can be qualitatively equivalent to those with learned ones, as well as the emergence of a symbiotic mutualistic effect between populations that forms when multiple actions are defined.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for games and combinatorial optimization, one of 15 directions in Deep reinforcement learning methods. Builds on 8 of its 68 references that are in the map. First posted on arXiv 3 Jul 2025.

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8 of 68 references.

References · 8

  1. 2020Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problemscited by 1,920
  2. 2017A Unified Game-Theoretic Approach to Multiagent Reinforcement Learningcited by 268
  3. 2022A Survey on Offline Reinforcement Learning: Taxonomy, Review, and Open Problemscited by 231
  4. 2022A Review of Off-Policy Evaluation in Reinforcement Learningcited by 95
  5. 2019Dealing with Non-Stationarity in Multi-Agent Deep Reinforcement Learningcited by 93
  6. 2020A Game Theoretic Framework for Model Based Reinforcement Learningcited by 73
  7. 2017Online Reinforcement Learning in Stochastic Gamescited by 67
  8. 2024Game Theory and Multi-Agent Reinforcement Learning : From Nash Equilibria to Evolutionary Dynamicscited by 1

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  1. 2024Enhancing Cooperation through Selective Interaction and Long-term Experiences in Multi-Agent Reinforcement Learning
  2. 2024Evolution of Societies via Reinforcement Learning
  3. 2024Mutation-Bias Learning in Games
  4. 2024The State-Action-Reward-State-Action Algorithm in Spatial Prisoner's Dilemma Game
  5. 2026An Evolutionary Computation Framework for Multi-Agent Q-Learning with Mean-Field Environmental Feedback
  6. 2026Evolutionary Stability Does Not Guarantee Learning Accessibility: A Multi-Agent Reinforcement Learning Perspective on Cooperation Emergence
  7. 2026The Role of Network Topology and Opponent Information in Shaping Cooperation in Multi-Agent Reinforcement Learning Systems
  8. 2026Spatial Pattern Formation from Multi-Agent Learning in Public Goods Dilemmas