Summary of Federated Offline Policy Optimization with Dual Regularization, by Sheng Yue et al.
Federated Offline Policy Optimization with Dual Regularization
by Sheng Yue, Zerui Qin, Xingyuan Hua, Yongheng Deng, Ju Ren
First submitted to arxiv on: 24 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 proposes a novel offline federated policy optimization algorithm, named DRPO, which enables distributed agents to collaboratively learn a decision policy from private and static data without interacting with the environment. The algorithm leverages dual regularization, incorporating both local behavioral policies and global aggregated policies, to cope with distributional shifts in offline FRL. Theoretical analysis demonstrates that by achieving the right balance of dual regularization, DRPO can counteract distributional shifts and ensure strict policy improvement. Extensive experiments validate the significant performance gains of DRPO over baseline methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Federated Reinforcement Learning (FRL) is a way for many devices to make smart decisions together. But existing FRL methods require lots of interactions with the environment, which can be expensive or even impossible in some real-world situations. To fix this problem, this paper proposes a new method called DRPO that allows devices to learn from their own private data without interacting with the environment. DRPO uses two types of policies: one for each device and one for all devices together. This helps balance the differences between devices’ behaviors and ensures they work well together. |
Keywords
» Artificial intelligence » Optimization » Regularization » Reinforcement learning
