Summary of Asynchronous Federated Reinforcement Learning with Policy Gradient Updates: Algorithm Design and Convergence Analysis, by Guangchen Lan et al.
Asynchronous Federated Reinforcement Learning with Policy Gradient Updates: Algorithm Design and Convergence Analysis
by Guangchen Lan, Dong-Jun Han, Abolfazl Hashemi, Vaneet Aggarwal, Christopher G. Brinton
First submitted to arxiv on: 9 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Distributed, Parallel, and Cluster Computing (cs.DC); Networking and Internet Architecture (cs.NI)
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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 The authors propose a novel asynchronous federated reinforcement learning (FedRL) framework called AFedPG, which enables multiple agents to collaborate and construct a global model using policy gradient updates. To address lagged policies in asynchronous settings, they design a delay-adaptive lookahead technique specifically for FedRL, achieving efficient handling of heterogeneous arrival times of policy gradients. Theoretical analysis shows that AFedPG enjoys a linear speedup with respect to the number of agents, outperforming both single-agent and synchronous FedPG methods. Time complexity is also improved from O(t_max/N) to O(sum(i=1 to N)(1/t_i))^{-1}, which becomes significant in large-scale federated settings. Empirical verification in four MuJoCo environments demonstrates AFedPG’s performance advantages in various computing heterogeneity scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AFedPG is a new way for many computers or agents to work together and learn from each other. The authors wanted to solve the problem of when some agents might be slow to send their updates, so they created a special technique to handle this. This helps the agents learn more efficiently and quickly. They also showed that AFedPG works better than previous methods in many cases, especially when there are lots of agents with different computing powers. |
Keywords
* Artificial intelligence * Reinforcement learning
