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insight - Cooperative Strategies in Multi-Agent Reinforcement Learning
Strategies for Promoting Cooperation in Multi-Agent Systems: Leveraging Game Theory and Mean-Field Equilibria
This work investigates strategies to invoke cooperation in game-theoretic scenarios, such as the iterated prisoner's dilemma, where agents must optimize both individual and group outcomes. It extends the analysis to N-player iterated prisoner's dilemma scenarios using mean-field game theory to establish equilibrium solutions and reward structures for infinitely large agent sets.
Enhancing Cooperation through Selective Interaction and Long-term Experiences in Multi-Agent Reinforcement Learning
Incorporating the ability for agents to selectively interact with neighbors and learn from long-term experiences can promote the emergence and maintenance of cooperation in multi-agent systems.
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