Summary of On the Linear Speedup Of Personalized Federated Reinforcement Learning with Shared Representations, by Guojun Xiong et al.
On the Linear Speedup of Personalized Federated Reinforcement Learning with Shared Representations
by Guojun Xiong, Shufan Wang, Daniel Jiang, Jian Li
First submitted to arxiv on: 22 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Optimization and Control (math.OC); 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 A novel federated reinforcement learning framework, dubbed Personalized Federated Reinforcement Learning (PFedRL), is introduced to address the issue of heterogeneous environments faced by different agents. Existing FedRL algorithms learn a single policy for individual agents, leading to poor performance in these scenarios. PFedRL-Rep, a specific instance of the framework, learns both shared feature representations among all agents and agent-specific weight vectors personalized to their local environments. The convergence of PFedTD-Rep, a temporal difference learning variant with linear representations, is analyzed and proven to exhibit a linear speedup with respect to the number of agents in the PFedRL setting. This is achieved through the application of federated two-timescale stochastic approximation with Markovian noise. Experimental results demonstrate improved learning in heterogeneous settings and better generalization to new environments using both PFedTD-Rep and its extension to control settings based on deep Q-networks (DQN). |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers developed a way for different agents to work together and learn from each other without sharing all their data. This helps when the agents are in different environments, which can make it hard for them to learn together. They created a new framework called Personalized Federated Reinforcement Learning (PFedRL) that allows agents to share some information but keep their own private experiences. The team showed that this approach works better than previous methods and can even help agents generalize to new situations they haven’t seen before. |
Keywords
* Artificial intelligence * Generalization * Reinforcement learning
