General Sum Markov Game

General-sum Markov games model multi-agent interactions where agents have diverse, potentially conflicting objectives, extending beyond simpler zero-sum or potential games. Current research focuses on developing decentralized learning algorithms, often employing actor-critic methods or optimistic follow-the-regularized-leader approaches, to efficiently find approximate Nash equilibria or correlated equilibria, addressing challenges like equilibrium bias and sample complexity. These advancements are crucial for improving the scalability and efficiency of multi-agent reinforcement learning, with implications for diverse applications ranging from autonomous systems to societal-scale simulations.