M_s_2o_6_k3gn.zip

: Learning to Control Autonomous Fleets via Sample-Efficient Deep Reinforcement Learning

: The authors introduce a decentralized training method with centralized execution that handles the large, dynamic scale of urban transport networks. 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

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 Paper Overview : Optimizing the dispatching and rebalancing

: Optimizing the dispatching and rebalancing of autonomous vehicle fleets (e.g., ride-sharing services) to minimize wait times and maximize efficiency.

: Filippos Christianos, Georgios Papoudakis, Aris Filos, and Stefano V. Albrecht.