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

By Shiyu Huang, Wenze Chen, Longfei Zhang, Ziyang Li, Fengming Zhu, Deheng Ye, Tingling Chen, Jun Zhu. 2021

30 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Economics and Econometrics, Reinforcement Learning

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Yu Wang
2. Chao Yu
3. Alexandre M. Bayen
4. Eugene Vinitsky
5. Akash Velu
6. Yi Wu
7. Sergey Levine

In Economics and Econometrics

1. Eugene Vinitsky
2. Alexandre M. Bayen
3. Akash Velu
4. Chao Yu
5. Yi Wu
6. Yu Wang

In Reinforcement Learning

1. Yu Wang
2. Alexandre M. Bayen
3. Chao Yu
4. Eugene Vinitsky
5. Akash Velu
6. Yi Wu
7. Sergey Levine

Citations in the corpus

• The Surprising Effectiveness of PPO in Cooperative Multi-Agent Games (2021, methodology): “• GRF : QMix [27] and SOTA methods including CDS [20] and TiKick [16].”