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End-to-End Autonomous Navigation Based on Deep Reinforcement Learning with a Survival Penalty Function
An end-to-end approach to autonomous navigation that is based on deep reinforcement learning (DRL) with a survival penalty function is proposed in this paper. Two actor–critic (AC) frameworks, namely, deep deterministic policy gradient (DDPG) and twin-delayed DDPG (TD3), are employed to enable a non...
| Autores principales: | , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
MDPI
2023
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| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10610759/ https://www.ncbi.nlm.nih.gov/pubmed/37896743 http://dx.doi.org/10.3390/s23208651 |