Enhancing Pokémon VGC Player Performance: Intelligent Agents Through Deep Reinforcement Learning and Neuroevolution-Reference-Cited by-同舟云学术

Enhancing Pokémon VGC Player Performance: Intelligent Agents Through Deep Reinforcement Learning and Neuroevolution

Published:2024 Issue: Volume: Page:275-294
ISSN:0302-9743
Container-title:Lecture Notes in Computer Science
language:en
Short-container-title:

Author:

Rodriguez Gian^ORCID,Villanueva Edwin^ORCID,Baldeón Johan^ORCID

Publisher

Springer Nature Switzerland

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-60692-2_19

Reference16 articles.

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2. Aljaafreh, A., Al-Oudat, N.: Development of a computer player for seejeh (a.k.a seega, siga, kharbga) board game with deep reinforcement learning. In: Procedia Computer Science. vol. 160, pp. 241–247 (2019). https://doi.org/10.1016/j.procs.2019.09.463

3. Arun, E., Rajesh, H., Chakrabarti, D., Cherala, H., George, K.: Monopoly using reinforcement learning. In: IEEE Region 10 Annual International Conference, Proceedings/TENCON, pp. 858–862 (2019). https://doi.org/10.1109/TENCON.2019.8929523

4. Barros, P., Sciutti, A.: All by myself: learning individualized competitive behavior with a contrastive reinforcement learning optimization. Neural Netw. 150, 364–376 (2022). https://doi.org/10.1016/j.neunet.2022.03.013

5. Chen, K., Lin, E.: Gotta train’em all: learning to play pokemon showdown with reinforcement learning (2018). https://cs230.stanford.edu/projects_fall_2018/reports/12447633.pdf