Introduction to Reinforcement Learning

Majid Ghasemi, Dariush Ebrahimi
8/13/2024
cs.AIcs.LG

No official code found. Best implementation: dennybritz/reinforcement-learning (22,157 stars, MIT, last commit 2023-07-13).

Abstract

Reinforcement Learning (RL), a subfield of Artificial Intelligence (AI), focuses on training agents to make decisions by interacting with their environment to maximize cumulative rewards. This paper provides an overview of RL, covering its core concepts, methodologies, and resources for further learning. It offers a thorough explanation of fundamental components such as states, actions, policies, and reward signals, ensuring readers develop a solid foundational understanding. Additionally, the paper presents a variety of RL algorithms, categorized based on the key factors such as model-free, model-based, value-based, policy-based, and other key factors. Resources for learning and implementing RL, such as books, courses, and online communities are also provided. By offering a clear, structured introduction, this paper aims to simplify the complexities of RL for beginners, providing a straightforward pathway to understanding.

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Code Implementations(1)

dennybritz/reinforcement-learningCommunity100%
MIT

Implementation of Reinforcement Learning Algorithms. Python, OpenAI Gym, Tensorflow. Exercises and Solutions to accompany Sutton's Book and David Silver's course.

22,1576,103Python, Jupyter NotebookAug 24, 20163 years agoMIT

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Cite this paper

@article{ghasemi2024introduction,
  title  = {Introduction to Reinforcement Learning},
  author = {Majid Ghasemi and Dariush Ebrahimi},
  year   = {2024},
  eprint = {2408.07712},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2408.07712v3}
}

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