On Reinforcement Learning for Full-Length Game of StarCraft

• Main Authors: Pang, Zhen-Jia, Liu, Ruo-Ze, Meng, Zhou-Yu, Zhang, Yi, Yu, Yang, Lu, Tong
• Format: Conference Proceeding
• Language: English
• ISSN: 2159-5399; 2374-3468
• DOI: 10.1609/aaai.v33i01.33014691

StarCraft II poses a grand challenge for reinforcement learning. The main difficulties include huge state space, varying action space, long horizon, etc. In this paper, we investigate a set of techniques of reinforcement learning for the full-length game of StarCraft II. We investigate a hierarchical approach, where the hierarchy involves two levels of abstraction. One is the macro-actions extracted from expert’s demonstration trajectories, which can reduce the action space in an order of magnitude yet remain effective. The other is a two-layer hierarchical architecture, which is modular and easy to scale. We also investigate a curriculum transfer learning approach that trains the agent from the simplest opponent to harder ones. On a 64×64 map and using restrictive units, we train the agent on a single machine with 4 GPUs and 48 CPU threads. We achieve a winning rate of more than 99% against the difficulty level-1 built-in AI. Through the curriculum transfer learning algorithm and a mixture of combat model, we can achieve over 93% winning rate against the most difficult noncheating built-in AI (level-7) within days. We hope this study could shed some light on the future research of large-scale reinforcement learning.