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

Chapter 10. Financial Portfolio Management

A financial portfolio is the process of distribution of funds into different financial products. The implementation of deep learning for portfolio management has been a research sector in the artificial intelligence community. With the advancements in reinforcement learning there has been active research in creating finance model free reinforcement learning frameworks to produce end to end finance portfolio managing agents.

Portfolio management is a continuous decision making process of reallocating funds into numerous different financial products with an objective of maximizing the returns.

Traditional state-of-the-art online portfolio management approaches include:


Important Algorithms


  • Buy and Hold
  • Best Stock
  • Constant Rebalanced Portfolios

Follow the winner

  • Universal Portfolios
  • Exponential Gradient
  • Follow the Leader
  • Follow the Regularized Leader
  • Aggregating-type Algorithms

Follow the loser

  • Anti Correlation
  • Passive Aggressive Mean Reversion
  • Confidence...