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

DeepTraffic – MIT simulator for autonomous driving 

DeepTraffic ( was created for the course MIT 6.S094: Deep Learning for Self-Driving Cars at MIT taught by Lex Fridman. Course content and assignment is public. DeepTraffic gained a lot of popularity owing to its leaderboard. With over 13,000 submissions to date, DeepTraffic is highly competitive. The users have to write their neural networks in convnet.js (a framework created by Andrej Karpathy) in the coding ground present in the link mentioned at the start of the section. The agent with the maximum average speed tops the leaderboard.

Simulations such as DeepTraffic help train different approaches to make the car agent adapt to the simulated environment quickly. Moreover, the competitive element of it adds to better submissions over time, beating the past top scores. The competition makes it fun but in the real world a student can't test their deep reinforcement learning scripts. Therefore, DeepTraffic...