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We study the reinforcement learning problem of complex action control in theMulti-player Online Battle Arena (MOBA) 1v1 games. This problem involves farmore complicated state and action spaces than those of traditional 1v1 games,such as Go and Atari series, which makes it very difficult to search anypolicies with human-level performance. In this paper, we present a deepreinforcement learning framework to tackle this problem from the perspectivesof both system and algorithm. Our system is of low coupling and highscalability, which enables efficient explorations at large scale. Our algorithmincludes several novel strategies, including control dependency decoupling,action mask, target attention, and dual-clip PPO, with which our proposedactor-critic network can be effectively trained in our system. Tested on theMOBA game Honor of Kings, our AI agent, called Tencent Solo, can defeat topprofessional human players in full 1v1 games.

Mastering Complex Control in MOBA Games with Deep Reinforcement Learning