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

Why asynchronous methods?

Asynchronous methods for deep reinforcement learning was published in June 2016 by the combined team of Google DeepMind and MILA (  It was faster and was able to show good results on a multi-core CPU instead of using a GPU. Asynchronous methods also work on continuous as well as discrete action spaces.

If we recall the approach of deep Q-network, we use experience replay as a storage to store all the experiences, and then use a random sample from that to train our deep neural network, which in turn predicts maximum Q-value for the most favorable action. But, it has the drawbacks of high memory usage and heavy computation over time. The basic idea behind this was to overcome this issue. Therefore, instead of using experience replay, multiple instances of the environment are created and multiple agents asynchronously execute actions in parallel (shown in the following diagram):

High-level diagram of the asynchronous method in deep...