: 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
: Originally published in Proceedings of the 20th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2021) . Context of the File : A novel Deep Reinforcement Learning (DRL) approach
: Learning to Control Autonomous Fleets via Sample-Efficient Deep Reinforcement Learning Context of the File : Learning to Control
The .zip file contains the of the algorithms discussed in the paper. The research focuses on:
: Optimizing the dispatching and rebalancing of autonomous vehicle fleets (e.g., ride-sharing services) to minimize wait times and maximize efficiency.