Premium Hands-on Workshop: Reinforcement Learning, Concepts to Applications

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80 Bloor Street West

#Suite 500 Room D

Toronto, ON M5S 2V1


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Refund Policy

Refund Policy

Refunds up to 7 days before event

Eventbrite's fee is nonrefundable.

Event description
Learn the foundations of RL models, how to code them, while building models that can play games in PyTorch

About this Event

Workshop Overview

Recent RL breakthroughs made it easy enough for regular data scientists to develop and deploy RL solutions to their problems. This course enables participants to add powerful RL methods to their ML toolbox which will be of great importance for growing your future ML career. You will master applying RL methods to various problems that help you to deploy your AI solution for your problem. Both Theory and Hands-on. The course gives an overview of various RL methods such as Q-Learning, SARSA, to Depp Q-learning, and DDPG.

Important Dates

Early Bird Deadline: September 5, 2019

Refund Deadline: September 6, 2019

Sales end on: September 18, 2019

Please note that this workshop will happen on 3 separate evenings:

Session 1: September 19, 2019

Session 2: September 26, 2019

Session 3: October 3, 2019

"Why should I care about RL?"

RL has many applications in business/industry:

  • Robotics
  • Real-Time Bidding with Multi-Agent Reinforcement Learning in Display Advertising
  • Resource Management with Deep Reinforcement Learning
  • DRN: A Deep Reinforcement Learning Framework for News Recommendation
  • Reinforcement learning-based multi-agent system for network traffic signal control
  • A Reinforcement Learning Approach to Online Web System Auto-configuration
  • Optimizing Chemical Reactions with Deep Reinforcement Learning

Why you should attend

In this 3-session intensive workshop, we will bring you up to speed with everything needed to build a strong background in RL. It will be a combination of theory and hands-on applications in PyTorch.

This workshop is built on the instructors extensive experience in academia and industry on related topics.

This workshop is the first in its series and paves the way theoretically and technically for many application specific workshops to follow.

Target Audience

Data Scientists, Machine Learning Engineers, Software Engineers, Students, Any ML/DS practitioner with basic understanding in ML


  • Python, PyTorch (basic knowledge required)
  • Machine learning fundamentals, and basic knowledge on Deep Neural Networks.

Learning Outcomes

The goal of this workshop is to help you master Reinforcement Learning (RL) methods. You will start by building an AI that can independently play simple Atari games. As we progress, you will learn advanced RL methods to enhance your model to play more complicated games. These methods are crucial for deploying your own AI solution to your business problem.

Pre-workshop reading material


Learning Material

All participants will have access to the following learning material:

  • Slides from the sessions
  • Hands on notebooks
  • Video recording of the sessions (you can use the videos to watch the parts that you missed, or re-watch any parts that are still unclear for you; access to videos beyond one week after the workshop is available to be purchased; see tickets >> add-ons)


Florian Goebels

ML Scientist, BMO Capital Markets

Florian has been working as a data scientist for more than 7 years across the world from Russia to Germany, and all the way to Canada. He worked as a postdoctoral fellow at the University of Toronto. After his postdoc, Dr. Goebels join LoyaltyOne innovation team where he investigated novel applications of how deep learning can be used to improve your shopping experience. Currently, Dr. Goebels works as Machine Learning Scientist for BMO Capital Markets building advanced trading robots.

Course Modules

The workshop happens on 3 evenings, and covers the following topics:

Day 1: Reinforcement Fundamentals

  1. Epsilon Greedy method
  2. Dynamic Programming
  3. Monto Carlo based optimization
  4. Q-Learning
  5. Sarsa

Day 2: Deep Q learning

  1. Deep Q learning
  2. Double Deep Q learning
  3. Dueling Deep Q learning
  4. Rainbow

Day 3: Policy-based methods

  1. Vanilla Policy Gradient
  2. Advantage function
  3. Soft Actor-Critic model
  4. Deep deterministic policy gradients

█░ Kick-starter style

Please note that our technical workshops are run kick-starter style in the sense that they will only happen if a certain amount of funds are raised by the 2-week deadline prior to the event (Sept 5, 2019):

Funds raised so far to enable the workshop:

██████████ 100%

Learning Packages

You can customize your learning package. Click on tickets, select your base package (in-person or online), and then tailor your experience the way you want using add-ons.

There is early bird public discount starting at 40% and decreasing by 10% every week until the end of early bird on Sept 5th, 2019

Discount Codes

There are discount codes available

  • AISC members receive discounts according to their participation credit; refer to the slack channel for more detail

Referral Program

AISC Members, have personalized referral discount codes up to 50% that they can share with their friends and coworkers. Each code can be used 3 times. In order to ensure that people are using the code with your permission they need to enter your email address when registering

Non-members, can enjoy a group discount (10% on top of the current early bird discount) by forming groups of 3+ and registering together. So, go talk to your friends or coworkers right now so that you all save 10% more together.

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Date and Time



80 Bloor Street West

#Suite 500 Room D

Toronto, ON M5S 2V1


View Map

Refund Policy

Refunds up to 7 days before event

Eventbrite's fee is nonrefundable.

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