Summary of Introduction to Reinforcement Learning, by Majid Ghasemi and Dariush Ebrahimi
Introduction to Reinforcement Learning
by Majid Ghasemi, Dariush Ebrahimi
First submitted to arxiv on: 13 Aug 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 paper provides an overview of Reinforcement Learning (RL), a subfield of Artificial Intelligence (AI). It covers core concepts, methodologies, and resources for further learning. The abstract explains fundamental components such as states, actions, policies, and reward signals, ensuring readers develop a solid foundational understanding. The paper also presents various RL algorithms, categorized based on key factors like model-free, model-based, value-based, policy-based, and other key factors. Additionally, the paper provides resources for learning and implementing RL, including books, courses, and online communities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Reinforcement Learning is a way to train artificial intelligence agents to make good decisions. The paper explains how it works and what are the most important parts. It also talks about different types of algorithms that can be used. The goal is to help people who are new to this field understand the basics and learn more. The paper provides resources for learning and implementing RL, like books and online communities. |
Keywords
* Artificial intelligence * Reinforcement learning
