Book Image

Hands-On Intelligent Agents with OpenAI Gym

By : Palanisamy P
Book Image

Hands-On Intelligent Agents with OpenAI Gym

By: Palanisamy P

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)

Implementing an Intelligent Agent for Optimal Control using Deep Q-Learning

In the previous chapter, we implemented an intelligent agent that used Q-learning to solve the Mountain Car problem from scratch in about seven minutes on a dual-core laptop CPU. In this chapter, we will implement an advanced version of Q-learning called deep Q-learning, which can be used to solve several discrete control problems that are much more complex than the Mountain Car problem. Discrete control problems are (sequential) decision-making problems in which the action space is discretized into a finite number of values. In the previous chapter, the learning agent used a 2-dimensional state-space vector as the input, which contained the information about the position and velocity of the cart to take optimal control actions. In this chapter, we will see how we can implement a learning agent that takes...