Tensorflow Neural Networks using Deep Q-Learning Techniques

3.1
10 個評分
提供方
Coursera Project Network
在此指導項目中,您將:

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).

Clock2 hours
Advanced高級設置
Cloud無需下載
Video分屏視頻
Comment Dots英語(English)
Laptop僅限桌面

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

Artificial Intelligence (AI)Intelligent AgentUnsupervised LearningTensorflowReinforcement Learning

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:

  1. Overview of Q-Learning Agents, Tensorflow, and OpenAI Gym

  2. Understand Deep-Q Learning Theory

  3. Building a Tensorflow Model

  4. Understand Activation Functions and Model Input

  5. Describe Keras Initializers and Optimizers

  6. Write Memory, Policy, and Action Functions

  7. Write The Training Function

  8. 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

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常見問題

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