Summary of Cuda2: An Approach For Incorporating Traitor Agents Into Cooperative Multi-agent Systems, by Zhen Chen and Yong Liao and Youpeng Zhao and Zipeng Dai and Jian Zhao
CuDA2: An approach for Incorporating Traitor Agents into Cooperative Multi-Agent Systems
by Zhen Chen, Yong Liao, Youpeng Zhao, Zipeng Dai, Jian Zhao
First submitted to arxiv on: 25 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR); Multiagent Systems (cs.MA)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper tackles the issue of cooperative multi-agent reinforcement learning (CMARL) being vulnerable to adversarial perturbations. The authors introduce a novel method for creating more realistic attacks by injecting “traitor” agents into the CMARL system, which they model as a Traitor Markov Decision Process (TMDP). To improve the training efficiency of these traitors, they propose the Curiosity-Driven Adversarial Attack (CuDA2) framework. CuDA2 incorporates a pre-trained Random Network Distillation module to encourage traitors to explore unencountered states, allowing for more effective attacks on victim agents’ policies. The authors demonstrate the effectiveness of their framework through extensive experiments on various scenarios from the SMAC benchmark. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Cooperative learning between artificial intelligence (AI) systems is a growing area of research. However, this kind of collaboration can be vulnerable to malicious attacks. In this paper, scientists introduce a new way to create more realistic and effective attacks by introducing “bad” agents into the system. These bad agents are trained using the same rules as the good AI systems, but with a twist: their goal is to sabotage the good AI’s performance. The researchers created a special framework that helps these bad agents become better at attacking the good AI. They tested this framework on different scenarios and found it to be more effective than other methods. |
Keywords
» Artificial intelligence » Distillation » Reinforcement learning
