Add topic "Qlearning" Accepted
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Add Intro to Qlearning in RL
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 Intro to Qlearning in RL
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 Interactive
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 20210430
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 In this tutorial, we aim to provide readers with a highlevel overview of the fundamentals of RL as well as example code in Python, introducing the OpenAI Gym library. We begin with building intuitions about what is considered an RL problem and we introduce formal definitions as well as key terminologies that are used to describe and model an RL application. In parallel, we will focus on solving a concrete example of an RL problem (CartPole) using a classic RL algorithm called Qlearning.
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 https://deepnote.com/@kene7bd/IntrotoQlearninginRL4R450s6_RVKJIC2xiqs71g
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Add Qlearning
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 Qlearning
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 Qlearning is a modelfree reinforcement learning algorithm to learn the value of an action in a particular state. It does not require a model of the environment (hence "modelfree"), and it can handle problems with stochastic transitions and rewards without requiring adaptations. For any finite Markov decision process (FMDP), Qlearning finds an optimal policy in the sense of maximizing the expected value of the total reward over any and all successive steps, starting from the current state. Qlearning can identify an optimal actionselection policy for any given FMDP, given infinite exploration time and a partlyrandom policy. "Q" refers to the function that the algorithm computes  the expected rewards for an action taken in a given state.
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 https://en.wikipedia.org/?curid=1281850
Topic  v1  current (v1) 
Add Reinforcement learning
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 Reinforcement learning
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 Reinforcement learning (RL) is an area of machine learning concerned with how intelligent agents ought to take actions in an environment in order to maximize the notion of cumulative reward. Reinforcement learning is one of three basic machine learning paradigms, alongside supervised learning and unsupervised learning. Reinforcement learning differs from supervised learning in not needing labelled input/output pairs be presented, and in not needing suboptimal actions to be explicitly corrected. Instead the focus is on finding a balance between exploration (of uncharted territory) and exploitation (of current knowledge).
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 https://en.wikipedia.org/?curid=66294
Topic  v1  current (v1) 
Add Qlearning treated in Intro to Qlearning in RL
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 treated in
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Add Qlearning used by Reinforcement learning
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 used by
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Add Deep learning parent of Reinforcement learning
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 parent of
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