Deep Deterministic Policy Gradient-Based Autonomous Driving for Mobile Robots in Sparse Reward Environments

In this paper, we propose a deep deterministic policy gradient (DDPG)-based path-planning method for mobile robots by applying the hindsight experience replay (HER) technique to overcome the performance degradation resulting from sparse reward problems occurring in autonomous driving mobile robots....

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Detalles Bibliográficos
Autores principales: Park, Minjae, Lee, Seok Young, Hong, Jin Seok, Kwon, Nam Kyu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9787388/
https://www.ncbi.nlm.nih.gov/pubmed/36559941
http://dx.doi.org/10.3390/s22249574

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9787388/
https://www.ncbi.nlm.nih.gov/pubmed/36559941
http://dx.doi.org/10.3390/s22249574

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