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Training agents in multi-agent competitive games presents significantchallenges due to their intricate nature. These challenges are exacerbated bydynamics influenced not only by the environment but also by opponents'strategies. Existing methods often struggle with slow convergence andinstability. To address this, we harness the potential of imitation learning tocomprehend and anticipate opponents' behavior, aiming to mitigate uncertaintieswith respect to the game dynamics. Our key contributions include: (i) a newmulti-agent imitation learning model for predicting next moves of the opponents-- our model works with hidden opponents' actions and local observations; (ii)a new multi-agent reinforcement learning algorithm that combines our imitationlearning model and policy training into one single training process; and (iii)extensive experiments in three challenging game environments, including anadvanced version of the Star-Craft multi-agent challenge (i.e., SMACv2).Experimental results show that our approach achieves superior performancecompared to existing state-of-the-art multi-agent RL algorithms.

Mimicking To Dominate: Imitation Learning Strategies for Success in Multiagent Competitive Games