Learning Battles in ViZDoom via Deep Reinforcement Learning
Kun Shao, Dongbin Zhao, Nannan Li, Yuanheng Zhu · 2018
First-person shooter (FPS) video games play an important role in game artificial intelligence (AI). In this paper, we present an effective deep reinforcement learning (DRL) method to learn battles in ViZDoom. Our approach utilizes the actor-critic with Kronecker-factored trust region (ACKTR), a sample-efficient and computationally inexpensive DRL method. We train our ACKTR agents in two battle scenarios, and compare with the advantage actor-critic (A2C) baseline agent. The experimental results demonstrate that DRL methods successfully teach agents to battle in these scenarios. In addition, the ACKTR agents significantly outperform the A2C agents in terms of all the metrics by a significant margin.