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

Reinforcement learning for autonomous driving

The challenge posed by autonomous driving cannot be solved by a full supervised learning approach owing to strong interactions with the environment and multiple obstacles and maneuvers (discussed previously) in the environment. The reward mechanism of reinforcement learning has to be highly effective so that the agent is very cautious about the safety of the individual inside and all the obstacles outside, whether it's humans, animals, or any ongoing construction.

One of the approaches to rewards could be:

  • Agent vehicle collides with the vehicle in front: High negative reward
  • Agent vehicle maintains safer distance from both front and rear end: Positive reward
  • Agent vehicle maintains unsafe distance: Moderate negative reward
  • Agent vehicle is closing the distance: Negative reward
  • Agent vehicle speeds up: Decreasing the positive reward as the speed increases and negative when it crosses the speed limit

Incorporating recurrent neural networks (RNNs) to...