Multi-Behavior Multi-Agent Reinforcement Learning for Informed Search via Offline Training-Reference-Cited by-同舟云学术

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Multi-Behavior Multi-Agent Reinforcement Learning for Informed Search via Offline Training

Published:2024-04-29 Issue: Volume:35 Page:19-26
ISSN:
Container-title:2024 20th International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT)
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Author:

Huang Songjun¹,Sun Chuanneng¹,Wang Ruo-Qian²,Pompili Dario¹

Affiliation:

1. Rutgers University,Department of Electrical and Computer Engineering,New Brunswick,NJ,USA

2. Rutgers University,Department of Civil and Environmental Engineering,New Brunswick,NJ,USA

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10621442/10621052/10621513.pdf?arnumber=10621513

Reference32 articles.

1. A semantically-informed multirobot system for exploration of relevant areas in search and rescue settings

2. Regionally accelerated batch informed trees (RABIT*): A framework to integrate local information into optimal path planning

3. Goal-LBP: Goal-Based Local Behavior Guided Trajectory Prediction for Autonomous Driving

4. SonAIr: Real-Time Deep Learning For Underwater Acoustic Spectrum Sensing and Classification

5. A Formal Basis for the Heuristic Determination of Minimum Cost Paths

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Adaptive versus predictive techniques in underwater acoustic communication networks;Computer Networks;2024-10

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