Meta Game

Meta-game analysis in multi-agent reinforcement learning focuses on understanding and predicting the strategic interactions between learning agents, aiming to improve cooperation and efficiency in complex scenarios. Current research emphasizes developing efficient algorithms, such as opponent-shaping methods and policy space response oracles (PSRO), to find optimal strategies within these interactions, often employing techniques like bootstrapping for robust evaluation. These advancements are significant because they address limitations of traditional reinforcement learning approaches in multi-agent settings, leading to improved performance in applications like negotiation and game playing.