Summary of A Comprehensive Survey Of Reinforcement Learning: From Algorithms to Practical Challenges, by Majid Ghasemi et al.
A Comprehensive Survey of Reinforcement Learning: From Algorithms to Practical Challenges
by Majid Ghasemi, Amir Hossein Moosavi, Dariush Ebrahimi
First submitted to arxiv on: 28 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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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 presented survey provides an in-depth analysis of Reinforcement Learning (RL) algorithms, ranging from tabular methods to advanced Deep Reinforcement Learning (DRL) techniques. The comprehensive review categorizes and evaluates these algorithms based on key criteria such as scalability, sample efficiency, and suitability. The paper highlights the strengths and weaknesses of each method in various settings and offers practical insights into selecting and implementing RL algorithms, addressing common challenges like convergence, stability, and the exploration-exploitation dilemma. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research explores how machines can learn from their mistakes to make better decisions. It looks at many different ways that computers can use Reinforcement Learning (RL) to solve problems on its own. The paper helps people choose the right RL approach for a specific task by comparing the strengths and weaknesses of each method. |
Keywords
» Artificial intelligence » Reinforcement learning
