Reinforcement Learning

Reinforcement learning (RL) is an area of machine learning concerned with how software 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 labeled input/output pairs be presented, and in not needing sub-optimal actions to be explicitly corrected. Instead, the focus is on finding a balance between exploration (of uncharted territory) and exploitation (of current knowledge).

from Reinforcement Learning - Wikipedia

This topic includes the following resources and journeys:



Why Choose Model-Based Reinforcement Learning?

Mathworks - Brian Douglas
15 min

What is the difference between model-free and model-based reinforcement learning? Explore the differences and results as the learning models are applied to balancing a cart/pole system as an...

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Multi-agent reinforcement learning: An overview

L. Bus ̧oniu, R. Babusˇka, and B. De Schutter
Peer Reviewed Paper

From the abstract:

Multi-agent systems can be used to address problems in a variety of do- mains, including robotics, distributed control, telecommunications, and economics. The...

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Reinforcement Learning: An Introduction

Richard S. Sutton and Andrew G. Barto

From the book introduction:

The idea that we learn by interacting with our environment is probably the first to occur to us when we think about the nature of learning. When an infant...

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Reinforcement Learning with MATLAB.

Mohammad Dehghani, Rifat Sipahi, Sahil S. Belsare

This repository contains series of modules to get started with Reinforcement Learning with MATLAB.

It is divided into 4 stages.

In Stage 1, we start with learning RL concepts by...

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