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({\lambda}) (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.

本文提出了一种强化学习和课程迁移学习方法，用于在StarCraft的微观管理中控制多个单位。通过定义高效的状态表示，并采用参数共享多智能体梯度下降Sarsa算法，使用神经网络作为函数逼近器来评估动作价值函数，建立奖励函数，使用迁移学习方法将模型推广到更具挑战性的情境，并鼓励协作行为，成功地在小规模情境中将内置AI击败。在大规模情境中，使用课程迁移学习方法逐步训练一组单位，并在目标情境中显示出优越的性能。

使用强化学习和课程迁移学习进行星际争霸微观管理