| Summary: | In this article, we present a comparative study of Artificial Intelligence training methods, in the context of a racing video game. The algorithms Proximal Policy Policy Optimization (PPO), Generative Adversarial Imitation Learning (GAIL) and Behavioral Cloning (BC), present in the Machine Learning Agents (ML-Agents) toolkit have been used in several scenarios. We measured their learning capability and performance in terms of speed, correct level traversal, number of training steps required and we explored ways to improve their performance. These algorithms prove to be suitable for racing games and the toolkit is highly accessible within the ML-Agents toolkit.
Received by the editors: 23 September 2021.
2010 Mathematics Subject Classification. 91A10, 68T05.
1998 CR Categories and Descriptors. I.2.1 [Artificial intelligence]: Applications and Expert Systems – Games; K.8.0 [Personal computing]: General – Gaming.
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