We are very excited about our work "Why so pessimistic? Estimating uncertainties for offline RL through ensembles, and why their independence matters." (with Shane Gu and Ofir Nachum) which was accepted at the Offline RL Workshop at NeurIPS 2021.
Our work "EMaQ: Expected-Max Q-Learning Operator for Simple Yet Effective Offline and Online RL" (with Dale Schuurmans and Shane Gu) was accepted at ICML 2021.
Our paper "A Divergence Minimization Perspective on Imitation Learning Methods" (with Richard Zemel and Shane Gu) received the Best Paper Award at the Conference on Robot Learning (CoRL) 2019!
This semester I am interning with Corey Lynch and Pierre Sermanet at Google Brain Robotics in Mountainview
Our paper "A Divergence Minimization Perspective on Imitation Learning Methods" (with Richard Zemel and Shane Gu) was accepted as an oral at CoRL 2019!
Our paper "SMILe: Scalable Meta Inverse Reinforcement Learning through Context-Conditional Policies" (with Shane Gu and Richard Zemel) was accepted as a poster at NeurIPS 2019!
Our paper "SMILe: Scalable Meta Inverse Reinforcement Learning through Context-Conditional Policies" (with Shane Gu and Richard Zemel) was accepted as an oral presentation to the Imitation, Intent, and Interaction (I3) Workshop at ICML 2019!
Our paper "Interpreting Imitation Learning Methods Under a Divergence Minimization Perspective" (with Shane Gu and Richard Zemel) was accepted to the Imitation, Intent, and Interaction (I3) Workshop at ICML 2019!
Our paper "Interpreting Imitation Learning Methods Under a Divergence Minimization Perspective" (with Shane Gu and Richard Zemel) was accepted to the Deep Generative Models for Highly Structured Data Workshop at ICLR 2019!