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

Temporal difference rule

Firstly, temporal difference (TD) is the difference of the value estimates between two time steps. It is different from the outcome-based Monte Carlo approach where a full look ahead till the end of the episode is done in order to update the learning parameters. In case of temporal difference learning, only one step look ahead is done and a value estimate of the state at the next step is used to update the current state's value estimate. Thus, learning parameters update along the way. Different rules to approach temporal difference learning are the TD(1), TD(0), and TD(

) rules. The basic notion in all the approaches is that the value estimate of the next step is used to update the current state's value estimate.

TD(1) rule

TD(1) incorporates the concept of eligibility trace. Let's go through the pseudo code of the approach and then we will discuss it in detail:

Episode T
    For all s, At the start of the episode : e(s) = 0 and 
: (at step t)