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Reinforcement learning - Wikipedia
https://en.wikipedia.org/wiki/Reinforcement_learning
webReinforcement learning ( RL) is an interdisciplinary area of machine learning and optimal control concerned with how an intelligent agent ought to take actions in a dynamic environment in order to maximize the cumulative reward. Reinforcement learning is one of three basic machine learning paradigms, alongside supervised learning and ...
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Reinforcement Learning Made Simple (Part 1): Intro to Basic …
https://towardsdatascience.com/reinforcement-learning-made-simple-part-1-intro-to-basic-concepts-and-terminology-1d2a87aa060
webOct 16, 2020 · Deep Q Networks (Our first deep-learning algorithm. A step-by-step walkthrough of exactly how it works, and why those architectural choices were made.) Policy Gradient (Our first policy-based deep-learning algorithm.) Actor-Critic (Sophisticated deep-learning algorithm which combines the best of Deep Q Networks and Policy Gradients.)
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Reinforcement Learning Algorithms: An Overview and …
https://arxiv.org/pdf/2209.14940.pdf
webWe classify RL algorithms according to the number of the states and action types available in the environment into three main categories: 1) a limited number of states and discrete actions, 2) an unlimited number of states and discrete actions, and 3) an unlimited number of states and continuous actions.
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Reinforcement Learning 101. Learn the essentials of Reinforcement…
https://towardsdatascience.com/reinforcement-learning-101-e24b50e1d292
webMar 19, 2018 · Reinforcement Learning (RL) is a type of machine learning technique that enables an agent to learn in an interactive environment by trial and error using feedback from its own actions and experiences.
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6 Reinforcement Learning Algorithms Explained | by Kay Jan …
https://towardsdatascience.com/6-reinforcement-learning-algorithms-explained-237a79dbd8e
webNov 25, 2022 · These 6 algorithms are the basic algorithms that help form the base understanding of Reinforcement Learning. There are more effective Reinforcement Learning algorithms such as Deep Q Network (DQN), Deep Deterministic Policy Gradient (DDPG), and other algorithms that have more practical applications.
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An Introduction to Deep Reinforcement Learning - Hugging Face
https://huggingface.co/blog/deep-rl-intro
webPublished May 4, 2022. Update on GitHub. ThomasSimonini Thomas Simonini. osanseviero Omar Sanseviero. What is Reinforcement Learning? Chapter 1 of the Deep Reinforcement Learning Class with Hugging Face 🤗. ⚠️ A new updated version of this article is available here 👉 https://huggingface.co/deep-rl-course/unit1/introduction.
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Evolving Reinforcement Learning Algorithms - Google Blog
https://blog.research.google/2021/04/evolving-reinforcement-learning.html
webApr 22, 2021 · In our paper “ Evolving Reinforcement Learning Algorithms ”, accepted at ICLR 2021, we show that it’s possible to learn new, analytically interpretable and generalizable RL algorithms by using a graph representation and applying optimization techniques from the AutoML community.
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Reinforcement Learning: An Introduction - University of …
https://inst.eecs.berkeley.edu/~cs188/sp20/assets/files/SuttonBartoIPRLBook2ndEd.pdf
weberal directions. Reinforcement learning has gradually become one of the most active research areas in machine learning, arti cial intelligence, and neural net-work research. The eld has developed strong mathematical foundations and impressive applications. The computational study of reinforcement learning is
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[2209.14940] Reinforcement Learning Algorithms: An Overview …
https://arxiv.org/abs/2209.14940
webSep 29, 2022 · Reinforcement Learning Algorithms: An Overview and Classification. Fadi AlMahamid, Katarina Grolinger. The desire to make applications and machines more intelligent and the aspiration to enable their operation without human interaction have been driving innovations in neural networks, deep learning, and other machine learning …
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The Art of Reinforcement Learning - Springer
https://link.springer.com/book/10.1007/978-1-4842-9606-6
webDec 8, 2023 · This book provides a deep dive into the core concepts, mathematics, and algorithms of reinforcement learning through practical examples. The Art of Reinforcement Learning: Fundamentals, Mathematics, and Implementations with Python | …
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