Mastering Complex Control in MOBA Games with Deep Reinforcement Learning

Understand

We study the reinforcement learning problem of complex action control in the Multi-player Online Battle Arena (MOBA) 1v1 games.

• This problem involves far more complicated state and action spaces than those of traditional 1v1 games, such as Go and Atari series, which makes it very difficult to search any policies with human-level performance.
• In this paper, we present a deep reinforcement learning framework to tackle this problem from the perspectives of both system and algorithm.
• Our system is of low coupling and high scalability, which enables efficient explorations at large scale.