Amy Zhang

Amy Zhang shares her work on Invariant Causal Prediction for Block MDPs, Multi-Task Reinforcement Learning with Context-based Representations, MBRL-Lib, shares insight on generalization on RL, and more!

Show Notes

Amy Zhang is a postdoctoral scholar at UC Berkeley and a research scientist at Facebook AI Research. She will be starting as an assistant professor at UT Austin in Spring 2023. 

Featured References 

Invariant Causal Prediction for Block MDPs 
Amy Zhang, Clare Lyle, Shagun Sodhani, Angelos Filos, Marta Kwiatkowska, Joelle Pineau, Yarin Gal, Doina Precup 

Multi-Task Reinforcement Learning with Context-based Representations 
Shagun Sodhani, Amy Zhang, Joelle Pineau 

MBRL-Lib: A Modular Library for Model-based Reinforcement Learning 
Luis Pineda, Brandon Amos, Amy Zhang, Nathan O. Lambert, Roberto Calandra 


Additional References 

• Amy Zhang - Exploring Context for Better Generalization in Reinforcement Learning @ UCL DARK 
• ICML 2020 Poster session: Invariant Causal Prediction for Block MDPs 
• Clare Lyle - Invariant Prediction for Generalization in Reinforcement Learning @ Simons Institute 

Creators & Guests

Host

Robin Ranjit Singh Chauhan

🌱 Head of Eng @AgFunder 🧠 AI:Reinforcement Learning/ML/DL/NLP🎙️Host @TalkRLPodcast 💳 ex-@Microsoft ecomm PgmMgr 🤖 @UWaterloo CompEng 🇨🇦 🇮🇳

What is TalkRL: The Reinforcement Learning Podcast?

TalkRL podcast is All Reinforcement Learning, All the Time.
In-depth interviews with brilliant people at the forefront of RL research and practice.
Guests from places like MILA, OpenAI, MIT, DeepMind, Berkeley, Amii, Oxford, Google Research, Brown, Waymo, Caltech, and Vector Institute.
Hosted by Robin Ranjit Singh Chauhan.