Summary of The Definitive Guide to Policy Gradients in Deep Reinforcement Learning: Theory, Algorithms and Implementations, by Matthias Lehmann
The Definitive Guide to Policy Gradients in Deep Reinforcement Learning: Theory, Algorithms and Implementations
by Matthias Lehmann
First submitted to arxiv on: 24 Jan 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 A holistic overview of on-policy policy gradient algorithms in deep reinforcement learning is provided, encompassing both theoretical foundations and practical implementations. The continuous version of the Policy Gradient Theorem is proved, along with convergence results and a comprehensive discussion of prominent algorithms. These are compared on continuous control environments, highlighting the benefits of regularization. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper provides a detailed look at policy gradient algorithms in deep reinforcement learning. It explains how these algorithms work, from the theoretical side to the practical implementations. The authors prove a key theorem, show that the algorithms converge, and compare them on different types of problems. They also talk about why regularization is helpful. |
Keywords
* Artificial intelligence * Regularization * Reinforcement learning
