Multi-agent reinforcement learning approach based on reduced value function approximations-Reference-Cited by-同舟云学术

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Multi-agent reinforcement learning approach based on reduced value function approximations

Published:2017-10 Issue: Volume: Page:
ISSN:
Container-title:2017 IEEE International Symposium on Robotics and Intelligent Sensors (IRIS)
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Author:

Abouheaf Mohammed,Gueaieb Wail

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/8241060/8250084/08250107.pdf?arnumber=8250107

Cited by 12 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. Rationality-bounded adaptive learning in multi-agent dynamic games;Knowledge-Based Systems;2023-05

2. Mix-attention approximation for homogeneous large-scale multi-agent reinforcement learning;Neural Computing and Applications;2022-10-07

3. Security Analysis of Poisoning Attacks Against Multi-agent Reinforcement Learning;Algorithms and Architectures for Parallel Processing;2022

4. An Adaptive Fuzzy Reinforcement Learning Cooperative Approach for the Autonomous Control of Flock Systems;2021 IEEE International Conference on Robotics and Automation (ICRA);2021-05-30

5. Responding to Illegal Activities Along the Canadian Coastlines Using Reinforcement Learning;IEEE Instrumentation & Measurement Magazine;2021-04

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