Summary of Overcoming the Curse Of Dimensionality in Reinforcement Learning Through Approximate Factorization, by Chenbei Lu et al.
Overcoming the Curse of Dimensionality in Reinforcement Learning Through Approximate Factorization
by Chenbei Lu, Laixi Shi, Zaiwei Chen, Chenye Wu, Adam Wierman
First submitted to arxiv on: 12 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 paper addresses the curse of dimensionality in Reinforcement Learning (RL) algorithms, which arises when dealing with large-scale problems. To overcome this challenge, the authors propose a novel approach that leverages task-specific model structures to improve sample efficiency. They introduce two RL algorithms: one model-based and another model-free, both utilizing variance-reduced Q-learning. The proposed methods provide theoretical guarantees for improved sample complexity, which is particularly important when dealing with large state-action spaces. Experimental results on synthetic MDP tasks and a real-world wind farm control problem demonstrate the practicality of these approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps solve a big problem in artificial intelligence called the “curse of dimensionality.” It’s like trying to find a specific book in a huge library, but instead of books, it’s complex data. The authors found a way to break down this complexity into smaller, more manageable pieces. They created two new ways for machines to learn from experience and make better decisions. These methods are tested on simulated problems and a real-world power grid control issue, showing they can be used in practice. |
Keywords
» Artificial intelligence » Reinforcement learning
