Summary of Federated Reinforcement Learning with Constraint Heterogeneity, by Hao Jin et al.
Federated Reinforcement Learning with Constraint Heterogeneity
by Hao Jin, Liangyu Zhang, Zhihua Zhang
First submitted to arxiv on: 6 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 a Federated Reinforcement Learning (FedRL) problem with constraint heterogeneity. The goal is to develop a policy that satisfies multiple constraints while training agents are distributed across different environments, each with limited access to constraint signals. This scenario is relevant in large language model fine-tuning and healthcare applications. To address this challenge, the authors propose federated primal-dual policy optimization methods based on traditional policy gradient techniques. They introduce local Lagrange functions for each agent to perform local policy updates and schedule periodic communications among agents. Two instances of these algorithms are explored: FedNPG and FedPPO. The paper shows that FedNPG achieves global convergence with an O(1/√T) rate, and FedPPO efficiently solves complex learning tasks using deep neural networks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper tries to solve a big problem in artificial intelligence called Federated Reinforcement Learning (FedRL). Imagine you have many smart robots or computers that need to work together to learn how to make good decisions. Each robot or computer is in a different environment and can only see a little bit of what’s going on, but they all need to agree on the same set of rules. This is important for things like fine-tuning language models and helping doctors make good decisions. The authors came up with new ways for these robots or computers to work together and learned how to make their decisions better over time. |
Keywords
» Artificial intelligence » Fine tuning » Large language model » Optimization » Reinforcement learning
