Book Image

Reinforcement Learning with TensorFlow

By : Sayon Dutta
Book Image

Reinforcement Learning with TensorFlow

By: Sayon Dutta

Overview of this book

Reinforcement learning (RL) allows you to develop smart, quick and self-learning systems in your business surroundings. It's an effective method for training learning agents and solving a variety of problems in Artificial Intelligence - from games, self-driving cars and robots, to enterprise applications such as data center energy saving (cooling data centers) and smart warehousing solutions. The book covers major advancements and successes achieved in deep reinforcement learning by synergizing deep neural network architectures with reinforcement learning. You'll also be introduced to the concept of reinforcement learning, its advantages and the reasons why it's gaining so much popularity. You'll explore MDPs, Monte Carlo tree searches, dynamic programming such as policy and value iteration, and temporal difference learning such as Q-learning and SARSA. You will use TensorFlow and OpenAI Gym to build simple neural network models that learn from their own actions. You will also see how reinforcement learning algorithms play a role in games, image processing and NLP. By the end of this book, you will have gained a firm understanding of what reinforcement learning is and understand how to put your knowledge to practical use by leveraging the power of TensorFlow and OpenAI Gym.
Table of Contents (21 chapters)
Title Page
Packt Upsell

Chapter 6. Asynchronous Methods

So far, we have covered most of the important topics, such as the Markov Decision Processes, Value Iteration, Q-learning, Policy Gradients, deep-Q networks, and Actor Critic Algorithms. These form the core of the reinforcement learning algorithms. In this chapter, we will continue our search from where we left off in Actor Critic Algorithms, and delve into the advanced asynchronous methods used in deep reinforcement learning, and its most famous variant, the asynchronous advantage actor-critic algorithm, better known as the A3C Algorithm.

But, before we start with the A3C algorithm, let's revise the basics of the Actor Critic Algorithm covered in Chapter 4, Policy Gradients. If you remember, the Actor Critic Algorithm has two components:

  • Actor
  • Critic

The Actor takes the current environment state and determines best action to take, while the Critic plays a policy-evaluation role by taking in the environment state and action, and returns a score depicting how good...