LLMs Empowering RL Agents

Hosted by Amir Feizpour and Matthew Taylor

Fri, Mar 21, 2025

4:00 PM UTC (45 minutes)

Virtual (Zoom)

Free to join

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Go deeper with a course

Build Multi-Agent Applications - A Bootcamp
Amir Feizpour, PhD and Abhimanyu Anand

What you'll learn

How LLMs enhance RL agents' reasoning and adaptability.

How to build smarter agents for real-world applications.

Skills necessary to leverage RL + LLMs in use cases.

Why this topic matters

This talk explores how LLMs can enhance reinforcement learning (RL) agents by improving reasoning, adaptability, and communication. By bridging language understanding with decision-making, this synergy unlocks smarter, more capable agents for real-world applications. Attendees will gain insights into leveraging LLMs to advance their professional AI and automation skillsets.

You'll learn from

Amir Feizpour

Founder @ Aggregate Intellect

Amir Feizpour is the founder, CEO, and Chief Scientist at Aggregate Intellect building a generative business brain for service and science based companies. Amir has built and grown a global community of 5000+ AI practitioners and researchers gathered around topics in AI research, engineering, product development, and responsibility. Prior to this, Amir was an NLP Product Lead at Royal Bank of Canada. Amir held a research position at University of Oxford conducting experiments on quantum computing resulting in high profile publications and patents. Amir holds a PhD in Physics from University of Toronto. Amir also serves the AI ecosystem as an advisor at MaRS Discovery District, works with several startups as fractional chief AI officer, and engages with a wide range of community audiences (business executives to hands-on developers) through training and educational programs. Amir leads Aggregate Intellect’s R&D via several academic collaborations. 

Matthew Taylor

Professor of Computing Science at the University of Alberta

Dr. Matthew Taylor is a Fellow and Canada CIFAR AI Chair at Amii and a Professor of Computing Science at the University of Alberta. He is the Director of the Intelligent Robot Learning (IRL) Lab and a Principal Investigator at the Reinforcement Learning & Artificial Intelligence (RLAI) Lab, at the University of Alberta.

Taylor’s research focuses on developing intelligent agents, physical or virtual entities that interact with their environments. His main goals are to enable individual agents, and teams of agents, to learn tasks in real-world environments that are not fully known when the agents are designed. Current approaches that his teams are investigating include improving reinforcement learning through demonstrations, teaching reinforcement learning systems through action advice, and training agents with discrete human feedback.

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