Analysis of the possibilities for using machine learning algorithms in the Unity environment


Abstract

Reinforcement learning algorithms are gaining popularity, and their advancement is made possible by the presence of tools to evaluate them. This paper concerns the applicability of machine learning algorithms on the Unity platform using the Unity ML-Agents Toolkit library. The purpose of the study was to compare two algorithms: Proximal Policy Optimization and Soft Actor-Critic. The possibility of improving the learning results by combining these algorithms with Generative Adversarial Imitation Learning was also verified. The results of the study showed that the PPO algorithm can perform better in uncomplicated environments with non-immediate rewards, while the additional use of GAIL can improve learning performance.


Keywords

reinforcement learning; imitation learning; Unity

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Published : 2021-09-30


Litwynenko, K., & Plechawska-Wójcik, M. (2021). Analysis of the possibilities for using machine learning algorithms in the Unity environment. Journal of Computer Sciences Institute, 20, 197-204. https://doi.org/10.35784/jcsi.2680

Karina Litwynenko  karina.litwynenko@pollub.edu.pl
  Poland
Małgorzata Plechawska-Wójcik 
Lublin University of Technology  Poland
https://orcid.org/0000-0003-1055-5344