Summary of Reinforcement Learning: An Overview, by Kevin Murphy
Reinforcement Learning: An Overview
by Kevin Murphy
First submitted to arxiv on: 6 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
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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 abstract presents an overview of deep reinforcement learning and sequential decision-making, exploring value-based, policy-gradient, and model-based methods. The paper covers various topics, including the intersection of reinforcement learning with large language models. Key techniques and approaches are discussed, highlighting their applications and relevance to real-world problems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research gives a broad look at how machines learn by making decisions one after another. It shows what’s happening in the field right now and why it matters. The paper talks about different ways computers make choices, including looking for good values and learning from patterns. There’s even a mention of using these methods with big language models. |
Keywords
* Artificial intelligence * Reinforcement learning
