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Hands-On Intelligent Agents with OpenAI Gym : Your Guide to Developing AI Agents Using Deep Reinforcement Learning.
Walks through the hands-on process of building intelligent agents from the basics and all the way up to solving complex problems including playing Atari games and driving a car autonomously in the CARLA simulator. Discusses various learning environments and how to transform real-world problems into...
| Clasificación: | Libro Electrónico |
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| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham :
Packt Publishing Ltd,
2018.
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| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Cover; Title Page; Copyright and Credits; Dedication; Packt Upsell; Contributors; Table of Contents; Preface; Chapter 1: 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.
- Chapter 2: Reinforcement Learning and Deep Reinforcement LearningWhat 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 algorithmsSummary; Chapter 3: 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; Summary; Chapter 4: 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; Summary; Chapter 5: 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 hyperparametersImplementing 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; Summary; Chapter 6: Implementing an Intelligent Agent for Optimal Control using Deep Q-Learning; Improving the Q-learning agent.