Both Evolutionary Algorithms (EAs) and Reinforcement Learning Algorithms (RLAs) have proven successful in policy optimisation tasks, however, there is scarce literature comparing their strengths and weaknesses. This makes it difficult to determine which group of algorithms is best suited for a task. This paper presents a comparison of two EAs and two RLAs in solving EvoMan - a video game playing benchmark. We test the algorithms both with and without noise introduction in the initialisation of multiple video game environments. We demonstrate that EAs reach a similar performance to RLAs in the static environments, but when noise is introduced the performance of EAs drops drastically while the performance of RLAs is much less affected.

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© 2022 Massachusetts Institute of Technology Published under a Creative Commons Attribution 4.0 International (CC BY 4.0) license
2022
Massachusetts Institute of Technology
This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. For a full description of the license, please visit https://creativecommons.org/licenses/by/4.0/legalcode.
Issue Section:
General Conference