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

Understand

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.