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 9. Reinforcement Learning in Autonomous Driving

In this chapter, we will cover different approaches researchers are working on to make end-to-end autonomous driving possible. We have seen many companies, such as Google, Tesla, Uber, Mercedes Benz, Audi, Volvo, Bosch, and many more enter the domain of self-driving cars. For the AI community, end-to-end autonomous driving will be the next milestone to achieve on the route to artificial general intelligence (AGI).

Looking at the current trend in automotive industry, we witness the following:

  • Environment and climate friendly electric cars are increasing
  • Monetization through cab aggregator service and carpooling, that is, ride sharing
  • Disruptive research on autonomous vehicles using AI and cloud power

Key Lego blocks of autonomous driving are as follows:

  • Sensor fusion (sensors can be camera, LIDAR, RADAR, GPS, and so on)
  • Object detection and classification
  • Vehicular path planning—which action to take such as steer left or right, accelerate, or...