Summary of Caesar: Enhancing Federated Rl in Heterogeneous Mdps Through Convergence-aware Sampling with Screening, by Hei Yi Mak et al.
CAESAR: Enhancing Federated RL in Heterogeneous MDPs through Convergence-Aware Sampling with Screening
by Hei Yi Mak, Flint Xiaofeng Fan, Luca A. Lanzendörfer, Cheston Tan, Wei Tsang Ooi, Roger Wattenhofer
First submitted to arxiv on: 29 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 Federated Reinforcement Learning (FedRL) is a machine learning technique that enables value-based agents to learn from diverse Markov Decision Processes (MDPs). Existing FedRL methods aggregate the agents’ learning by averaging their value functions, but this strategy is suboptimal in heterogeneous environments. To address this issue, our study introduces Convergence-AwarE SAmpling with scReening (CAESAR), an aggregation scheme that combines convergence-aware sampling with a screening mechanism. CAESAR selectively assimilates knowledge from more proficient agents learning in identical MDPs, enhancing the overall learning efficiency. We validated our hypothesis using GridWorld and FrozenLake-v1 tasks, demonstrating the effectiveness of CAESAR in heterogeneous environments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to learn new skills on different platforms or games. You want to improve your performance by learning from others who are already good at it. That’s what Federated Reinforcement Learning (FedRL) does! It helps value-based agents, like computer programs, learn together and share knowledge across different environments. Existing methods don’t work well when the environments are very different, but our study introduces a new way to combine learning called CAESAR. This method makes sure that agents only take advice from those who are doing better in similar situations, which leads to faster and more efficient learning. |
Keywords
* Artificial intelligence * Machine learning * Reinforcement learning
