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


In this chapter, we looked at one of the recently published approaches to using deep reinforcement learning in financial portfolio management. We looked at a problem statement in financial portfolio management, the objectives of a portfolio manager, and mapped the problem statement to a reinforcement learning task. We also learned about different financial metrics for benchmarking performance and different existing online portfolio management approaches. This research topic of automating financial portfolio using deep reinforcement learning is among the most challenging tasks to solve in the AI community. Therefore, apart from the approach covered in this chapter do try to study other traditional machine learning approaches in algorithmic trading.

In the next chapter, we will study the use of reinforcement learning in robotics, the current challenges, and their proposed solutions.