Summary of Global Convergence Guarantees For Federated Policy Gradient Methods with Adversaries, by Swetha Ganesh et al.
Global Convergence Guarantees for Federated Policy Gradient Methods with Adversaries
by Swetha Ganesh, Jiayu Chen, Gugan Thoppe, Vaneet Aggarwal
First submitted to arxiv on: 15 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Optimization and Control (math.OC)
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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 A Federated Reinforcement Learning (FRL) framework that enables multiple agents to develop a decision-making policy without sharing raw trajectories is proposed. However, the presence of adversarial agents can lead to catastrophic results if not addressed. To mitigate this issue, a policy gradient-based approach is developed that is robust to arbitrary values sent by malicious agents. The method achieves global convergence guarantees with general parametrization and demonstrates resilience against adversaries while maintaining optimal sample complexity. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Federated Reinforcement Learning (FRL) lets multiple agents work together without sharing their actions. But what if some of these agents are trying to cause trouble? A new approach is developed that can deal with this problem. It’s based on policy gradients and ensures the whole system works well, even when some agents are being naughty. This means we can trust the results and they’re efficient too! |
Keywords
* Artificial intelligence * Reinforcement learning
