Reinforcement Learning, Part 1: Introduction

This post is an introduction of what reinforcement learning is and how it can help you solve some control system problems which are too difficult to solve with traditional techniques. Reinforcement learning is a type of machine learning technique that has the potential of solving complex control problems. Reinforcement learning can be used to develop self-driving cars, autonomous robots and gaming applications.

Unlike with supervised or unsupervised learning, which operate using a static dataset, reinforcement learning works with data from a dynamic environment. The goal of reinforcement learning is not to cluster data or label data, but to find the best sequence of actions that will generate the most optimal outcome. The way reinforcement learning solves this problem is by allowing a piece of software called an agent to explore, interact with, and learn from an environment using a reward-based system to find an optimal set actions to achieve a set outcome.

This free E-book will take you through a more detailed explanation of reinforcement learning and how it can be implemented using MATLAB.

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