On Multi-Agent Learning in Team Sports Games
This work addresses the need for more sample-efficient and human-like AI in complex video games, though it is incremental as it builds on existing reinforcement learning methods.
The paper tackles the problem of training human-like and skilled agents in team sports games using a hierarchical approach, with preliminary results indicating promise for multi-agent learning.
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. While this is still work in progress, our preliminary results show that the presented approach holds promise for solving the posed multi-agent learning problem.