強化學習:藉由深度學習建立控制決策模型

演講摘要

強化學習是透過深度學習來解決控制問題的一種方法,它所使用的不是經過標記的資料,而是利用一個包含了與環境之間的互動,透過執行大量模擬,以嘗試錯誤的方式來學習的系統模型。模擬的資料被用來訓練由深度神經網路所代表的決策(policy),這項決策建立完成之後將能夠取代傳統的控制器及決策系統,為系統自動地進行控制決策。

藉由本段演講,你將了解如何利用MathWorks產品來進行強化學習,以及如何設置環境模型,定義決策以及與決策相關的各種超參數,並且透過平行運算來擴大訓練以提升決策訓練的效能。


Reinforcement Learning: Leveraging Deep Learning for Controls

Reinforcement learning allows you to solve control problems using deep learning but without using labeled data. Instead, it uses a model of your system that captures interactions in an environment and learns through performing multiple simulations. This simulation data is used to train a policy represented by a deep neural network that would then replace a traditional controller or decision-making system.

In this session, you will learn how to do reinforcement learning using MathWorks products as well as how to set up your environment models, define the policy and its various hyperparameters, and scale training through parallel computing to improve performance.