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

Why policy optimization methods?

In this section, we will cover the pros and cons of policy optimization methods over value-based methods. The advantages are as follows:

  • They provides better convergence.
  • They are highly effective in case of high-dimensional/continuous state-action spaces. If action spaces are very big then a max function in a value-based method will be computationally expensive. So, the policy-based method directly changes the policy by changing the parameters instead of solving the max function at each step.
  • Ability to learn stochastic policies.

The disadvantages associated with policy-based methods are as follows:

  • Converges to local instead of global optimum
  • Policy evaluation is inefficient and has high variance

We will discuss the approaches to tackle these disadvantages later in this chapter. For now, let's focus on the need for stochastic policies.

Why stochastic policy?

Let's go through two examples that will explain the importance of incorporating a stochastic policy compared...