Summary of Federated Control in Markov Decision Processes, by Hao Jin et al.
Federated Control in Markov Decision Processes
by Hao Jin, Yang Peng, Liangyu Zhang, Zhihua Zhang
First submitted to arxiv on: 7 May 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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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 research paper explores problems of federated control in Markov Decision Processes (MDPs). To tackle large-scale MDPs with limited agent capabilities, multiple learning agents are introduced to collaborate without sharing local experience. The agents operate within distinct regions of the state space during training, leading to heterogeneity and leakage probabilities affecting the learning process. The authors propose a novel Federated-Q protocol (FedQ) that aggregates knowledge from restricted regions and updates learning problems for further training. Theoretical analysis justifies FedQ’s correctness, sample complexity is studied for derived algorithms FedQ-X with RL oracle, and FedQ-SynQ. Experiments in various environments validate the efficiency of these methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this study, researchers work on solving big Markov Decision Process problems by letting multiple smart agents learn together without sharing what they’ve learned. Each agent can only explore a specific part of the problem space during training. This makes it challenging to understand how the different parts affect the learning process. To address this, the authors introduce a new way for agents to share knowledge and update their own learning goals. The study shows that this approach works well in theory and practice. |
