The Tea Time Talks 2021: Week Three

The Tea Time Talks are back! Throughout the summer, take in 20-minute talks on early-stage ideas, prospective research and technical topics delivered by students, faculty and guests. Presented by Amii and the RLAI Lab at the University of Alberta, the talks are a relaxed and informal way of hearing leaders in AI discuss future lines of research they may explore.

Watch select talks from the third week of the series now:

Michael Przystupa: Analyzing Neural Jacobian Methods in Applications of Visual Servoing and Kinematic Control

Abstract: Designing adaptable control laws that can transfer between different robots is a challenge because of kinematic and dynamic differences, as well as in scenarios where external sensors are used. In this talk, Michael Przystupa explains his team’s work empirically investigating a neural network's ability to approximate the Jacobian matrix for an application in Cartesian control schemes. Specifically, they are interested in approximating the kinematic Jacobian, which arises from kinematic equations mapping a manipulator’s joint angles to the end-effector’s location.

Alex Lewandowski: Disentangling Generalization in Reinforcement Learning using Contextual Decision Processes

Abstract: The way in which generalization is measured in Reinforcement Learning (RL) relies on concepts from supervised learning. Unlike a supervised learning model however, an RL agent must generalize across states, observations and actions from limited reward-based feedback. In this talk, Alex Lewandowski describes how their team reformulated the problem of generalization in RL within a single environment by considering contextual decision processes with observations from a supervised learning dataset. The result is an MDP that, while simple, necessitates function approximation for state abstraction while providing precise ground-truth labels for optimal policies and value functions. They then characterize generalization in RL across different axes: state-space, observation-space and action-space. Using the MNIST dataset with a contextual decision process, they rigorously evaluate generalization of DQN and QR-DQN in observation and action space with both online and offline learning.

RLAI Panel 2

This talk features a panel of reinforcement learning (RL) researchers -- all Amii Fellows, Canada CIFAR AI Chairs and UAlberta professors. Michael Bowling moderates this panel featuring Rich Sutton, Martha White, Patrick Pilarski and Rupam Mahmood.

Like what you’re learning here? Take a deeper dive into the world of RL with the Reinforcement Learning Specialization, offered by the University of Alberta and Amii. Taught by Martha White and Adam White, this specialization explores how RL solutions help solve real-world problems through trial-and-error interaction, showing learners how to implement a complete RL solution from beginning to end. Enroll in this specialization now!

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