更新时间：2021-07-23 19:23:09
封面
Title Page
Copyright and Credits
Hands-On Intelligent Agents with OpenAI Gym
Dedication
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Why subscribe?
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Contributors
About the author
About the reviewer
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Preface
Who this book is for
What this book covers
To get the most out of this book
Download the example code files
Download the color images
Conventions used
Get in touch
Reviews
Introduction to Intelligent Agents and Learning Environments
What is an intelligent agent?
Learning environments
What is OpenAI Gym?
Understanding the features of OpenAI Gym
Simple environment interface
Comparability and reproducibility
Ability to monitor progress
What can you do with the OpenAI Gym toolkit?
Creating your first OpenAI Gym environment
Creating and visualizing a new Gym environment
Summary
Reinforcement Learning and Deep Reinforcement Learning
What is reinforcement learning?
Understanding what AI means and what's in it in an intuitive way
Supervised learning
Unsupervised learning
Reinforcement learning
Practical reinforcement learning
Agent
Rewards
Environment
State
Model
Value function
State-value function
Action-value function
Policy
Markov Decision Process
Planning with dynamic programming
Monte Carlo learning and temporal difference learning
SARSA and Q-learning
Deep reinforcement learning
Practical applications of reinforcement and deep reinforcement learning algorithms
Getting Started with OpenAI Gym and Deep Reinforcement Learning
Code repository setup and configuration
Prerequisites
Creating the conda environment
Minimal install – the quick and easy way
Complete install of OpenAI Gym learning environments
Instructions for Ubuntu
Instructions for macOS
MuJoCo installation
Completing the OpenAI Gym setup
Installing tools and libraries needed for deep reinforcement learning
Installing prerequisite system packages
Installing Compute Unified Device Architecture (CUDA)
Installing PyTorch
Exploring the Gym and its Features
Exploring the list of environments and nomenclature
Nomenclature
Exploring the Gym environments
Understanding the Gym interface
Spaces in the Gym
Implementing your First Learning Agent - Solving the Mountain Car problem
Understanding the Mountain Car problem
The Mountain Car problem and environment
Implementing a Q-learning agent from scratch
Revisiting Q-learning
Implementing a Q-learning agent using Python and NumPy
Defining the hyperparameters
Implementing the Q_Learner class's __init__ method
Implementing the Q_Learner class's discretize method
Implementing the Q_Learner's get_action method
Implementing the Q_learner class's learn method
Full Q_Learner class implementation
Training the reinforcement learning agent at the Gym
Testing and recording the performance of the agent
A simple and complete Q-Learner implementation for solving the Mountain Car problem
Implementing an Intelligent Agent for Optimal Control using Deep Q-Learning
Improving the Q-learning agent
Using neural networks to approximate Q-functions
Implementing a shallow Q-network using PyTorch
Implementing the Shallow_Q_Learner
Solving the Cart Pole problem using a Shallow Q-Network
