Resilient Multi-agent Reinforcement Learning Using Medoid and Soft-medoid Based Aggregation
Author:
Affiliation:
1. Vanderbilt University,Institute for Software Integrated Systems,Nashville,USA
2. University of Texas at Dallas,Department of Systems Engineering,Richardson,USA
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9763554/9763488/09763602.pdf?arnumber=9763602
Reference45 articles.
1. The hidden vulnerability of distributed learning in byzantium;guerraoui;International Conference on Machine Learning,2018
2. Adversarial attacks in consensus-based multi-agent reinforcement learning
3. Adversarial attack and defense in reinforcement learning-from AI security view
4. Challenges and countermeasures for adversarial attacks on deep reinforcement learning;ilahi;IEEE Transactions on Artificial Intelligence,2021
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