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- W3201936151 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." @default.
- W3201936151 created "2021-10-11" @default.
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- W3201936151 date "2021-09-30" @default.
- W3201936151 modified "2023-10-14" @default.
- W3201936151 title "Analysis of the possibilities for using machine learning algorithms in the Unity environment" @default.
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- W3201936151 doi "https://doi.org/10.35784/jcsi.2680" @default.
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