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


The core of the proposed reinforcement learning framework is the Ensemble of Identical Independent Evaluators (EIIE) topology. Here, EIIE is a neural network that takes the asset history as the input and evaluates the potential growth of the asset in future. The evaluation score of each asset is used to calculate the portfolio weights for the next trading period.

The portfolio weights (which we will discuss later) are actually the market actions of the portfolio managing agent powered by reinforcement learning. An asset whose target weight is increased will be bought, while the assets with decreased target weights will be sold. Thus, the portfolio weights from the last period of trading are also fed as an input to EIIE. Therefore, the portfolio weights of each period are stored in portfolio vector memory (PVM). 

The EIIE is trained in by Online Stochastic Batch Learning (OSBL) where the reward functions of the reinforcement learning framework are the average logarithmic returns...