Summary of Towards Fast Rates For Federated and Multi-task Reinforcement Learning, by Feng Zhu et al.
Towards Fast Rates for Federated and Multi-Task Reinforcement Learning
by Feng Zhu, Robert W. Heath Jr., Aritra Mitra
First submitted to arxiv on: 9 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Systems and Control (eess.SY); Optimization and Control (math.OC)
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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 proposed Fast-FedPG algorithm addresses the challenges of heterogeneous agent interactions in Markov Decision Processes (MDPs). The algorithm handles intermittent communication and noisy policy gradients to achieve fast linear convergence and sub-linear rates with linear speedup. This outperforms existing methods that only provide asymptotic rates or generate biased policies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The Fast-FedPG algorithm is designed for a setting where each agent interacts with an MDP with different reward functions, representing heterogeneous objectives. The goal is to find a policy that maximizes the average of long-term cumulative rewards across environments. The algorithm’s bias-correction mechanism ensures convergence to a globally optimal policy without heterogeneity-induced bias. |
