: Originally published in Proceedings of the 20th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2021) . Context of the File
: Optimizing the dispatching and rebalancing of autonomous vehicle fleets (e.g., ride-sharing services) to minimize wait times and maximize efficiency.
: The authors introduce a decentralized training method with centralized execution that handles the large, dynamic scale of urban transport networks.
: Learning to Control Autonomous Fleets via Sample-Efficient Deep Reinforcement Learning
The .zip file contains the of the algorithms discussed in the paper. The research focuses on:
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
: 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.
M_s_2o_6_k3gn.zip Site
: Originally published in Proceedings of the 20th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2021) . Context of the File
: Optimizing the dispatching and rebalancing of autonomous vehicle fleets (e.g., ride-sharing services) to minimize wait times and maximize efficiency. M_S_2o_6_k3gn.zip
: The authors introduce a decentralized training method with centralized execution that handles the large, dynamic scale of urban transport networks. : Originally published in Proceedings of the 20th
: Learning to Control Autonomous Fleets via Sample-Efficient Deep Reinforcement Learning : Learning to Control Autonomous Fleets via Sample-Efficient
The .zip file contains the of the algorithms discussed in the paper. The research focuses on:
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
: 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.