In plain words: A shared policy lets a team of StarCraft units learn from a compact state description, with rewards balancing movement and attack, then moves from easy to hard fights. They win 100% of small battles against the built-in AI and beat other approaches in bigger ones.
Abstract · StarCraft Micromanagement with Reinforcement Learning and Curriculum Transfer Learning
Real-time strategy games have been an important field of game artificial intelligence in recent years. This paper presents a reinforcement learning and curriculum transfer learning method to control multiple units in StarCraft micromanagement. We define an efficient state representation, which breaks down the complexity caused by the large state space in the game environment. Then a parameter sharing multi-agent gradientdescent Sarsa(λ) (PS-MAGDS) algorithm is proposed to train the units. The learning policy is shared among our units to encourage cooperative behaviors. We use a neural network as a function approximator to estimate the action-value function, and propose a reward function to help units balance their move and attack. In addition, a transfer learning method is used to extend our model to more difficult scenarios, which accelerates the training process and improves the learning performance. In small scale scenarios, our units successfully learn to combat and defeat the built-in AI with 100% win rates. In large scale scenarios, curriculum transfer learning method is used to progressively train a group of units, and shows superior performance over some baseline methods in target scenarios. With reinforcement learning and curriculum transfer learning, our units are able to learn appropriate strategies in StarCraft micromanagement scenarios.
Kun Shao, Yuanheng Zhu, Dongbin Zhao
arXiv:1804.00810 · cs.AI, cs.LG, cs.MA · submitted Apr 3, 2018
abstract · pdf · html · 12 pages, 14 figures, accepted to IEEE Transactions on Emerging Topics in Computational Intelligence
I am not very surprised that machine learning has been able to successfully execute micromanagement. In my opinion the macro decisions are more interesting, since they typically require higher level reasoning (and comprehension of what the opponent is doing). We have yet to see an AI system that can successfully execute macro strategy when playing against a human opponent.