Tensorflow Neural Networks using Deep Q-Learning Techniques
Utilize OpenAI Gym for model training.
Construct and train a Neural Network in Tensorflow using Q-Learning techniques
Improve Q-Learning techniques with enhancements such as Dueling Q and Prioritized Experience Replay (PER).
By the end of this project you will learn how to train a reinforcement learning agent to play Atari video games autonomously using Deep Q-Learning with Tensorflow and OpenAI's Gym API. This project will familiarize you with the Gym interface and the process of training a Tensorflow-based neural network using Deep Q-Learning techniques. The methods you will learn in the course of this project will enable you to build reinforcement learning agents for any potential purpose and provide valuable experience in your Machine Learning and Artificial Intelligence development journey. Python experience is heavily recommended. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
您要培養的技能
分步進行學習
在與您的工作區一起在分屏中播放的視頻中，您的授課教師將指導您完成每個步驟：
Overview of Q-Learning Agents, Tensorflow, and OpenAI Gym
Understand Deep-Q Learning Theory
Building a Tensorflow Model
Understand Activation Functions and Model Input
Describe Keras Initializers and Optimizers
Write Memory, Policy, and Action Functions
Write The Training Function
Explore Enhancements such as Double Q, Dueling Q, and Prioritized Experience Replay
指導項目工作原理
您的工作空間就是瀏覽器中的雲桌面，無需下載
在分屏視頻中，您的授課教師會為您提供分步指導
常見問題
常見問題
如果我購買指導項目，會得到什麼？
指導項目可在台式設備和移動設備上學習嗎？
指導項目的講師是誰？
我能在完成指導項目後從中下載作品嗎？
我能夠退款嗎？退款政策是如何規定的？
有助學金嗎？
我能旁聽指導項目並免費觀看視頻部分嗎？
我需要具備多少經驗才能做這個指導項目？
我能直接通過 Web 瀏覽器來完成此指導項目，而不必安裝特殊軟件嗎？
指導項目的學習體驗如何？
還有其他問題嗎？請訪問 學生幫助中心。
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