Real World Game

Real-world game research focuses on developing and analyzing models and algorithms for multi-agent interactions in complex, asynchronous, and partially observable environments, moving beyond simplified game settings. Current efforts concentrate on improving multi-agent reinforcement learning algorithms, such as variations of fictitious play, and designing more realistic benchmark environments that capture the stochasticity, asymmetry, and flexible agent numbers of real-world scenarios. This research aims to enhance our understanding of strategic decision-making in complex systems and improve the performance of AI agents in collaborative and competitive tasks, with implications for areas like robotics, economics, and human-computer interaction.