Normal Form Game

Normal-form games model strategic interactions between agents by representing their choices and resulting payoffs in a matrix. Current research focuses on developing efficient algorithms for finding Nash equilibria (optimal strategy profiles) in various game settings, including multiplayer games with imperfect information and games with complex payoff structures, often employing techniques like regret matching, multiplicative weights updates, and deep reinforcement learning with residual networks. These advancements improve our understanding of strategic decision-making in complex systems and have implications for multi-agent systems, economics, and the design of more effective AI agents. The computational complexity of finding equilibria remains a significant challenge, leading to exploration of approximate solutions and alternative solution concepts like correlated equilibria.