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About A Concise Introduction To Reinforcement Learning: February 2018
This document provides a concise introduction to reinforcement learning which is a form of machine learning that involves an agent learning through trial-and-error interactions with an environment. It highlights some major successes of reinforcement learning, discusses key concepts like Markov decision processes and Bellman equations, and summarizes different algorithms used to solve reinforcement learning problems including Monte Carlo methods, temporal difference learning, and deep Q-networks. The introduction provides useful context on how reinforcement learning relates to and differs from other types of machine learning such as supervised and unsupervised learning.
Detailed Information
Author: | ['NirajDhotre'] |
---|---|
Publication Year: | 2020 |
Pages: | 12 |
Language: | English |
Format: | |
Price: | FREE |
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