Application of Deep Reinforcement Learning to NS-SHAFT Game Signal Control

Reinforcement learning (RL) with both exploration and exploit abilities is applied to games to demonstrate that it can surpass human performance. This paper mainly applies Deep Q-Network (DQN), which combines reinforcement learning and deep learning to the real-time action response of NS-SHAFT game...

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Detalles Bibliográficos
Autores principales: Chang, Ching-Lung, Chen, Shuo-Tsung, Lin, Po-Yu, Chang, Chuan-Yu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9317465/
https://www.ncbi.nlm.nih.gov/pubmed/35890943
http://dx.doi.org/10.3390/s22145265

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9317465/
https://www.ncbi.nlm.nih.gov/pubmed/35890943
http://dx.doi.org/10.3390/s22145265

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