I am exploring the use of Game Theory within multi-agent Reinforcement Learning (RL) frameworks for my PhD research. Specifically, I’m interested in understanding which game-theoretic approaches (e.g., Nash equilibrium, cooperative vs. non-cooperative strategies) can best optimize agent interactions in complex environments. Any insights on recent advancements, recommended algorithms, or case studies in this area would be greatly appreciated. Additionally, I'd like to know about potential pitfalls or limitations when implementing these strategies in large-scale, real-world simulations.