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

• URL: http://arxiv.org/abs/2110.04507v2
• Date: Tue, 12 Oct 2021 05:25:00 GMT
• Title: TiKick: Toward Playing Multi-agent Football Full Games from Single-agent Demonstrations
• Authors: Shiyu Huang, Wenze Chen, Longfei Zhang, Ziyang Li, Fengming Zhu, Deheng Ye, Ting Chen, Jun Zhu
• Abstract summary: Tikick is the first learning-based AI system that can take over the multi-agent Google Research Football full game. To the best of our knowledge, Tikick is the first learning-based AI system that can take over the multi-agent Google Research Football full game.
• Score: 31.596018856092513
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deep reinforcement learning (DRL) has achieved super-human performance on complex video games (e.g., StarCraft II and Dota II). However, current DRL systems still suffer from challenges of multi-agent coordination, sparse rewards, stochastic environments, etc. In seeking to address these challenges, we employ a football video game, e.g., Google Research Football (GRF), as our testbed 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 large replay dataset from the self-playing of single-agent experts, which are obtained from league training. We then developed a distributed learning system and new offline algorithms to learn a powerful multi-agent AI from the fixed single-agent dataset. To the best of our knowledge, Tikick is the first learning-based AI system that can take over the multi-agent Google Research Football full game, while previous work could either control a single agent or experiment on toy academic scenarios. Extensive experiments further show that our pre-trained model can accelerate the training process of the modern multi-agent algorithm and our method achieves state-of-the-art performances on various academic scenarios.

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