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Deep reinforcement learning (DRL) has achieved super-human performance oncomplex video games (e.g., StarCraft II and Dota II). However, current DRLsystems still suffer from challenges of multi-agent coordination, sparserewards, stochastic environments, etc. In seeking to address these challenges,we employ a football video game, e.g., Google Research Football (GRF), as ourtestbed and develop an end-to-end learning-based AI system (denoted as TiKick)to complete this challenging task. In this work, we first generated a largereplay dataset from the self-playing of single-agent experts, which areobtained from league training. We then developed a distributed learning systemand new offline algorithms to learn a powerful multi-agent AI from the fixedsingle-agent dataset. To the best of our knowledge, Tikick is the firstlearning-based AI system that can take over the multi-agent Google ResearchFootball full game, while previous work could either control a single agent orexperiment on toy academic scenarios. Extensive experiments further show thatour pre-trained model can accelerate the training process of the modernmulti-agent algorithm and our method achieves state-of-the-art performances onvarious academic scenarios.

TiKick: Towards Playing Multi-agent Football Full Games from Single-agent Demonstrations