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)


Being the final chapter of this book, this chapter provided summaries of key learning algorithms that are currently state of the art in this domain. We looked at the core concepts behind three different state-of-the-art algorithms, each with their own unique elements and their own categories (actor-critic/policy based/value-function based).

Specifically, we discussed the deep deterministic policy gradient algorithm, which is an actor-critic architecture method that uses a deterministic policy rather than the usual stochastic policy, and achieves good performance on several continuous control tasks.

We then looked at the PPO algorithm, which is a policy gradient-based method that uses a clipped version of the TRPO objective and learns a monotonically better and stable policy, and has been successfully used in very high-dimensional environments such as DOTA II.