Practical Reinforcement Learning for Data Scientists

Abstract: 

Reinforcement Learning (RL) studies how an agent can interact with its environment to learn a policy that maximizes expected cumulative rewards for a task. Recently, RL has experienced growth and interest due to use case results in robotics, finance, autonomous driving, business management, education, energy, healthcare, to name a few. In this tutorial, we focus on a technique called Deep Q Learning. DQL is a RL technique where rewards are cataloged and maximized while learning the best policy. We provide a gentle introduction to Deep Q Learning with a canonical example of winning a game called the cart-pole problem. In the next part of the tutorial, we will outline how DQL applies to a finance example, predicting the next best move (i.e., best purchase and sales of stocks) to maximize gains. This valuable tutorial will teach both deep learning and distributed computing (Ray) skills.

Session Outline
Lesson 1: Deep Q Learning - what is it and how does it work?
Familiarize yourself with the general concepts of reinforcement learning, deep learning and q-learning. You will learn how these concepts tie together in the industrial context, with an emphasis on practical application.

Lesson 2: Learning about Ray and RLLib Libraries for Distributed RL
Practice using the distributed computing library, Ray, and its scalable reinforcement learning library, RLLib. We will have an initial tutorial where we solve the cart-pole problem and while doing so learn about distributed RL.

Lesson 3: Reinforcement Learning for Financial Predictions
We will discuss practical applications of reinforcement learning, and its use in financial institutions. We will walk through an example of applying deep q learning to making the best next choice for stock market purchases or sales. We will demonstrate how scalable techniques can make reinforcement learning a practical tool for business problems.

Background Knowledge
Attendees should have familiarity with Python and typical ML packages (e.g., pandas, numpy, sklearn, torch). We also suggest that attendees understand the basics of distributed computing, deep learning, and reinforcement learning. We will provide optional readings on the necessary background ahead of the tutorial.

Bio: 

Jennifer Davis, Ph.D. is a Staff Field Data Scientist at Domino Data Labs, where she empowers clients on complex data science projects. She has completed two postdocs in computational and systems biology, trained at a supercomputing center at the University of Texas, Austin, and worked on hundreds of consulting projects with companies ranging from start-ups to the Fortune 100. Jennifer has previously presented topics for Association for Computing Machinery on LSTMs and Natural Language Generation and at conferences across the US and in Italy. Jennifer was part of a panel discussion for an IEEE conference on artificial intelligence in biology and medicine. She has practical experience teaching both corporate classes and at the college level.

Open Data Science

 

 

 

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