Papert Labs

Analytics | Augmentation | Automation

Reinforcement Learning with RAY
ACM KDD 2018

How did NY State government save $100 Million in 3 Years?
How can we build autonomous helicopters?
How do you improve the economic efficiency of high-end art auctions?


Update Aug 14, 2018: Check out the contents for the workshop here.

These are just few of the questions that Reinforcement Learning can help answer. In this tutorial, we will go over how to use Reinforcement Learning in the real world, from inception to implementation all the way to debugging. The first half of the tutorial will introduce and implement state of the art algorithms in RL (Bandits, DQN, A3C, PPO etc.). We will also have real-world case studies.

In the second half of the tutorial, we will discuss some of the latest advances in Distributed Reinforcement Learning and provide a hands-on tutorial of Ray, a framework for doing distributed machine learning, that has been adopted by some of the most innovative AI organizations and companies in the world. 

Name *
Describe your experience with Reinforcement Learning in the past. *