Reinforcement Learning Research with the Dopamine Framework
Reinforcement Learning Research with the Dopamine Framework

Abstract: 

A brief introduction to Reinforcement Learning (RL), and a walkthrough of using the Dopamine library for running RL experiments.

Session Outline
1. Introduction to Reinforcement Learning
2. Introduction to the Dopamine library
3. Work through an example to train an RL agent with Dopamine

Background Knowledge
Python, Linear algebra

Bio: 

Pablo Samuel Castro was born and raised in Quito, Ecuador, and moved to Montreal after high school to study at McGill. He stayed in Montreal for the next 10 years, finished his bachelors, worked at a flight simulator company, and then eventually obtained his masters and PhD at McGill, focusing on Reinforcement Learning. After his PhD Pablo did a 10-month postdoc in Paris before moving to Pittsburgh to join Google. He has worked at Google for over 8 years, and is currently a Staff Research Software Developer in Google Brain in Montreal, focusing on fundamental Reinforcement Learning research, as well as Machine Learning and Creativity. Aside from his interest in coding/AI/math, Pablo is actively trying to increase the presence of the latin american community in the AI research ecosystem. On the side, he's an active musician (https://www.psctrio.com/), loves running (5 marathons so far, including Boston!), and enjoys discussing politics and activism.