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
: Learning to Control Autonomous Fleets via Sample-Efficient Deep Reinforcement Learning
: Originally published in Proceedings of the 20th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2021) . Context of the File M_S_2o_6_k3gn.zip
The .zip file contains the of the algorithms discussed in the paper. The research focuses on:
: 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
: The authors introduce a decentralized training method with centralized execution that handles the large, dynamic scale of urban transport networks.
: Filippos Christianos, Georgios Papoudakis, Aris Filos, and Stefano V. Albrecht. The research focuses on: : A novel Deep
: Optimizing the dispatching and rebalancing of autonomous vehicle fleets (e.g., ride-sharing services) to minimize wait times and maximize efficiency.