Sequential Game

Sequential games model interactions where agents take turns making decisions, impacting subsequent choices and outcomes. Current research focuses on developing efficient algorithms, such as variations of coordinate descent and Hessian-based methods, to find optimal strategies (e.g., Nash equilibria) in increasingly complex scenarios, including those with imperfect information or multiple memory states. These advancements are crucial for improving AI agents' ability to cooperate or compete effectively in dynamic environments and have implications for diverse fields like multi-agent reinforcement learning, game theory, and even image processing. The development of robust and efficient algorithms for solving sequential games is driving progress in various applications, from optimizing resource allocation to enhancing human-AI collaboration.