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

Machine learning for autonomous driving

Firstly, in order to develop an end-to-end self-driving car we must know the development process at a high-level before delving into the use of reinforcement learning in the whole process. The following is a diagram that depicts the development process:

As shown in the preceding figure, the first step of the process is the collection of sensor data. Sensors comprise a camera, LIDAR, IMU, RADAR, GPS, CAN, and many more devices that can capture the state of the vehicle as well as the surrounding environment in the best possible way. After receiving these sensory signals, they are preprocessed, aggregated, and then prepared for sending to the next process, which includes machine learning (ML) and analysis in the data center. This step of implementing ML on the prepared sensory signals is a key part, which involves state estimation from the input data, thereby modeling it, predicting the possible future actions, and finally, the planning as per the predicted...