Creating Pro-Level AI for a Real-Time Fighting Game Using Deep Reinforcement Learning

TL;DR

This paper introduces a deep reinforcement learning approach with a novel curriculum and data skipping techniques to create high-level AI agents for a complex real-time fighting game, surpassing professional human players.

Contribution

It presents a practical RL method with a self-play curriculum and data skipping, achieving human-level performance in a modern fighting game, which was previously unachieved.

Findings

AI agents achieved 62% win rate against professional gamers.

The method creates diverse agent styles through reward shaping.

Data skipping improved data efficiency and exploration.

Abstract

Reinforcement learning combined with deep neural networks has performed remarkably well in many genres of games recently. It has surpassed human-level performance in fixed game environments and turn-based two player board games. However, to the best of our knowledge, current research has yet to produce a result that has surpassed human-level performance in modern complex fighting games. This is due to the inherent difficulties with real-time fighting games, including: vast action spaces, action dependencies, and imperfect information. We overcame these challenges and made 1v1 battle AI agents for the commercial game "Blade & Soul". The trained agents competed against five professional gamers and achieved a win rate of 62%. This paper presents a practical reinforcement learning method that includes a novel self-play curriculum and data skipping techniques. Through the curriculum, three different styles of agents were created by reward shaping and were trained against each other. Additionally, this paper suggests data skipping techniques that could increase data efficiency and facilitate explorations in vast spaces. Since our method can be generally applied to all two-player competitive games with vast action spaces, we anticipate its application to game development including level design and automated balancing.