Multiagent Reinforcement Learning: Rollout and Policy Iteration for POMDP with Application to Multi-Robot Problems-Reference-Cited by-同舟云学术

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Multiagent Reinforcement Learning: Rollout and Policy Iteration for POMDP with Application to Multi-Robot Problems

Published:2023 Issue: Volume: Page:1-20
ISSN:1552-3098
Container-title:IEEE Transactions on Robotics
language:
Short-container-title:IEEE Trans. Robot.

Author:

Bhattacharya Sushmita¹,Kailas Siva²,Badyal Sahil²,Gil Stephanie¹,Bertsekas Dimitri³

Affiliation:

1. REACT lab in the Computer Science Department at the School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA

2. REACT lab as external collaborators, USA

3. department of Computer, Information, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Control and Systems Engineering

Link

http://xplorestaging.ieee.org/ielx7/8860/4359257/10374262.pdf?arnumber=10374262

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. Multi-Level Action Tree Rollout (MLAT-R): Efficient and Accurate Online Multiagent Policy Improvement;2024 IEEE International Conference on Robotics and Automation (ICRA);2024-05-13

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