Deep Deterministic Policy Gradients with Transfer Learning Framework in StarCraft Micromanagement

摘要

This paper proposes an intelligent multi-agent approach in a real-time strategy game, StarCraft, based on the deep deterministic policy gradients (DDPG) techniques. An actor and a critic network are established to estimate the optimal control actions and corresponding value functions, respectively. A special reward function is designed based on the agents' own condition and enemies' information to help agents make intelligent control in the game. Furthermore, in order to accelerate the learning process, the transfer learning techniques are integrated into the training process. Specifically, the agents are trained initially in a simple task to learn the basic concept for the combat, such as detouring moving, avoiding and joining attacking. Then, we transfer this experience to the target task with a complex and difficult scenario. From the experiment, it is shown that our proposed algorithm with transfer learning can achieve better performance.