From the series: MATLAB Oil and Gas Conference 2019
Reinforcement learning allows you to solve control problems using deep learning without using labeled data. Instead, it uses a model of your system that captures the appropriate dynamics of the environment and learns through performing multiple simulations. This simulation data is used to train a policy which is often represented by a deep neural network that would then replace your traditional controller or decision-making system.
In this talk, you will learn how to use Reinforcement Learning Toolbox™ and other MathWorks products to set up your environment models, define the policy and its various hyperparameters, and scale training through parallel computing to improve performance.
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