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)

Deep Deterministic Policy Gradients

Deep Deterministic Policy Gradient (DDPG) is an off-policy, model-free, actor-critic algorithm and is based on the Deterministic Policy Gradient (DPG) theorem ( Unlike the deep Q-learning-based methods, actor-critic policy gradient-based methods are easily applicable to continuous action spaces, in addition to problems/tasks with discrete action spaces.

Core concepts

In Chapter 8, Implementing an Intelligent Autonomous Car Driving Agent Using the Deep Actor-Critic algorithm, we walked you through the derivation of the policy gradient theorem and reproduced the following for bringing in context:

You may recall that the policy we considered was a stochastic...