Hybrid Policy Learning for Multi-Agent Pathfinding

Hybrid Policy Learning for Multi-Agent Pathfinding

In this work we study the behavior of groups of autonomous vehicles, which are the part of the Internet of Vehicles systems. One of the challenging modes of operation of such systems is the case when the observability of each vehicle is limited and the global/local communication is unstable, e.g. in...

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Bibliographic Details
Main Authors: Alexey Skrynnik, Alexandra Yakovleva, Vasilii Davydov, Konstantin Yakovlev, Aleksandr I. Panov
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Access
Subjects:
Multiagent systems
path planning
machine learning
intelligent transportation systems
reinforcement learning
Monte-Carlo Tree Search
Online Access:https://ieeexplore.ieee.org/document/9532001/

Internet

https://ieeexplore.ieee.org/document/9532001/

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