ANALYSIS OF THE APPLICATION OF REINFORCEMENT LEARNING ALGORITHMS ON THE STARCRAFT II VIDEO GAME

Resumo

In recent years Machine Learning techniques have become the driving force behind the worldwide emergence of Artificial Intelligence, producing cost-effective and precise tools for pattern recognition and data analysis. A particular approach for the training of neural networks, Reinforcement Learning (RL), achieved prominence creating almost unbeatable artificial opponents in board games like Chess or Go, and also on video games. This paper gives an overview of Reinforcement Learning and tests this approach against a very popular real-time strategy game, Starcraft II. Our goal is to examine the tools and algorithms readily available for RL, also addressing different scenarios where a neural network can be linked to Starcraft II to learn by itself. This work describes both the technical issues involved and the preliminary results obtained by the application of two specific training strategies, A2C and DQN.