: Learning to Control Autonomous Fleets via Sample-Efficient Deep Reinforcement Learning
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
: 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. : Learning to Control Autonomous Fleets via Sample-Efficient
: Filippos Christianos, Georgios Papoudakis, Aris Filos, and Stefano V. Albrecht. dynamic scale of urban transport networks.
: The authors introduce a decentralized training method with centralized execution that handles the large, dynamic scale of urban transport networks.