Multiple Types of AI and Their Performance in Video Games-Reference-Cited by-同舟云学术

Multiple Types of AI and Their Performance in Video Games

Published:2022-07-03 Issue:1 Volume:67 Page:21-36
ISSN:2065-9601
Container-title:Studia Universitatis Babeș-Bolyai Informatica
language:
Short-container-title:Studia UBB Informatica

Author:

Prăjescu Iulian, ,Călin Alina Delia^ORCID,

Abstract

"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. Keywords and phrases: racing game, PPO, GAIL, behavioral cloning, AI in games. "

Publisher

Babes-Bolyai University

Subject

General Earth and Planetary Sciences,General Environmental Science

Reference23 articles.

1. "[1] Berndt, C., Watson, I., and Guesgen, H. Oasis: an open ai standard interface specification to support reasoning, representation and learning in computer games. In IJCAI-05 Workshop on Reasoning, Representation, and Learning in Computer Games (2005), Citeseer, pp. 19-24.

2. [2] Bhattacharyya, R., Wulfe, B., Phillips, D., Kuefler, A., Morton, J., Senanayake, R., and Kochenderfer, M. Modeling human driving behavior through generative adversarial imitation learning. arXiv preprint arXiv:2006.06412 (2020).

3. [3] Bojarski, M., Del Testa, D., Dworakowski, D., Firner, B., Flepp, B., Goyal, P., Jackel, L. D., Monfort, M., Muller, U., Zhang, J., et al. End to end learning for self-driving cars. arXiv preprint arXiv:1604.07316 (2016).

4. [4] Fan, X., Wu, J., and Tian, L. A review of artificial intelligence for games. Artificial Intelligence in China (2020), 298-303.

5. [5] Giusti, A., Guzzi, J., Cires，an, D. C., He, F.-L., Rodr'ıguez, J. P., Fontana, F., Faessler, M., Forster, C., Schmidhuber, J., Caro, G. D., Scaramuzza, D., and Gambardella, L. M. A machine learning approach to visual perception of forest trails for mobile robots. IEEE Robotics and Automation Letters 1, 2 (2016), 661-667.