On Multi-Agent Learning in Team Sports Games

Understand

In recent years, reinforcement learning has been successful in solving video games from Atari to Star Craft II.

• However, the end-to-end model-free reinforcement learning (RL) is not sample efficient and requires a significant amount of computational resources to achieve superhuman level performance.
• Model-free RL is also unlikely to produce human-like agents for playtesting and gameplaying AI in the development cycle of complex video games.
• In this paper, we present a hierarchical approach to training agents with the goal of achieving human-like style and high skill level in team sports games.