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

Proposed frameworks for autonomous driving

In this section, we will discuss a proposed deep reinforcement learning framework for autonomous driving given by El Sallab et. al 2017 ( 

The following is an architecture of end-to-end deep neural networks: 

End to End training of Deep Neural Networks for Autonomous Driving by El Sallab et. al 2017 (

Let's discuss the preceding architecture in detail. Inputs in this case are the aggregation of states of the environment over multiple timesteps. 

Spatial aggregation

The first unit of the architecture is the spatial aggregation network. It consists of two networks, each for the the following sub-processes:

  • Sensor fusion
  • Spatial features

The overall state includes the state of the vehicle as well as the state of the surrounding environment. The state of the vehicle includes position, geometric orientation, velocity, acceleration, current fuel left, current steering direction, and many...