In Chapter 6, Implementing an Intelligent Agent for Optimal Control using Deep Q-Learning, we implemented agents using deep Q-learning to solve discrete control tasks that involve discrete actions or decisions to be made. We saw how they can be trained to play video games such as Atari, just like we do: by looking at the game screen and pressing the buttons on the game pad/joystick. We can use such agents to pick the best choice given a finite set of choices, make decisions, or perform actions where the number of possible decisions or actions is finite and typically small. There are numerous real-world problems that can be solved with an agent that can learn to take optimal through to discrete actions. We saw some examples in Chapter 6, Implementing an Intelligent Agent for Optimal Discrete...
Hands-On Intelligent Agents with OpenAI Gym
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Hands-On Intelligent Agents with OpenAI Gym
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Overview of this book
Many real-world problems can be broken down into tasks that require a series of decisions to be made or actions to be taken. The ability to solve such tasks without a machine being programmed requires a machine to be artificially intelligent and capable of learning to adapt. This book is an easy-to-follow guide to implementing learning algorithms for machine software agents in order to solve discrete or continuous sequential decision making and control tasks.
Hands-On Intelligent Agents with OpenAI Gym takes you through the process of building intelligent agent algorithms using deep reinforcement learning starting from the implementation of the building blocks for configuring, training, logging, visualizing, testing, and monitoring the agent. You will walk through the process of building intelligent agents from scratch to perform a variety of tasks. In the closing chapters, the book provides an overview of the latest learning environments and learning algorithms, along with pointers to more resources that will help you take your deep reinforcement learning skills to the next level.
Table of Contents (12 chapters)
Preface
Free Chapter
Introduction to Intelligent Agents and Learning Environments
Reinforcement Learning and Deep Reinforcement Learning
Getting Started with OpenAI Gym and Deep Reinforcement Learning
Exploring the Gym and its Features
Implementing your First Learning Agent - Solving the Mountain Car problem
Implementing an Intelligent Agent for Optimal Control using Deep Q-Learning
Creating Custom OpenAI Gym Environments - CARLA Driving Simulator
Implementing an Intelligent - Autonomous Car Driving Agent using Deep Actor-Critic Algorithm
Exploring the Learning Environment Landscape - Roboschool, Gym-Retro, StarCraft-II, DeepMindLab
Exploring the Learning Algorithm Landscape - DDPG (Actor-Critic), PPO (Policy-Gradient), Rainbow (Value-Based)
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