Constrained Markov Game

Constrained Markov Games (CMGs) model multi-agent decision-making problems where agents must optimize their individual objectives while adhering to safety or resource constraints. Current research focuses on developing efficient algorithms, such as primal-dual methods and proximal-point updates, to find Nash equilibria (optimal strategies for all agents) in these games, particularly within the context of Markov Potential Games which offer some structural advantages. This research is significant because it enables the development of safe and robust AI agents for applications like autonomous driving and power grid management, where constraint satisfaction is crucial. The development of provably efficient algorithms and associated sample complexity bounds is a key area of ongoing investigation.