: A novel Deep Reinforcement Learning (DRL) approach that uses a hierarchical structure to improve "sample efficiency," meaning the system learns effective strategies using significantly less data than traditional methods.
The filename is the identifier for the supplementary code and data associated with the research paper "Learning to Control Autonomous Fleets via Sample-Efficient Deep Reinforcement Learning" . Paper Overview
: Filippos Christianos, Georgios Papoudakis, Aris Filos, and Stefano V. Albrecht.
The .zip file contains the of the algorithms discussed in the paper. The research focuses on:
: Learning to Control Autonomous Fleets via Sample-Efficient Deep Reinforcement Learning
: A novel Deep Reinforcement Learning (DRL) approach that uses a hierarchical structure to improve "sample efficiency," meaning the system learns effective strategies using significantly less data than traditional methods.
The filename is the identifier for the supplementary code and data associated with the research paper "Learning to Control Autonomous Fleets via Sample-Efficient Deep Reinforcement Learning" . Paper Overview M_S_2o_6_k3gn.zip
: Filippos Christianos, Georgios Papoudakis, Aris Filos, and Stefano V. Albrecht. : A novel Deep Reinforcement Learning (DRL) approach
The .zip file contains the of the algorithms discussed in the paper. The research focuses on: M_S_2o_6_k3gn.zip
: Learning to Control Autonomous Fleets via Sample-Efficient Deep Reinforcement Learning
Ваш комментарий успешно добавлен.
После проверки комментарий будет опубликован на сайте.