166 citations · 504 across the 15 of their papers we have counts for
15 papers
A perspective on fluid mechanical environments for challenges in reinforcement learning
Shruti Mishra, Michael Chang, Vamsi Spandan +1
We consider the challenge of developing agents that efficiently interact with high-dimensional, evolving environments, towards a view of practical reinforcement learning (RL) agent…
Im-Promptu: In-Context Composition from Image Prompts
Bhishma Dedhia, Michael Chang, Jake C. Snell +2
Large language models are few-shot learners that can solve diverse tasks from a handful of demonstrations. This implicit understanding of tasks suggests that the attention mechanis…
Neural Constraint Satisfaction: Hierarchical Abstraction for Combinatorial Generalization in Object Rearrangement
Michael Chang, Alyssa L. Dayan, Franziska Meier +3
Object rearrangement is a challenge for embodied agents because solving these tasks requires generalizing across a combinatorially large set of configurations of entities and their…
Human-Timescale Adaptation in an Open-Ended Task Space
Adaptive Agent Team, Jakob Bauer, Kate Baumli +25
Foundation models have shown impressive adaptation and scalability in supervised and self-supervised learning problems, but so far these successes have not fully translated to rein…
Object Representations as Fixed Points: Training Iterative Refinement Algorithms with Implicit Differentiation
Michael Chang, Thomas L. Griffiths, Sergey Levine
Iterative refinement -- start with a random guess, then iteratively improve the guess -- is a useful paradigm for representation learning because it offers a way to break symmetrie…
Modularity in Reinforcement Learning via Algorithmic Independence in Credit Assignment
Michael Chang, Sidhant Kaushik, Sergey Levine +1
Many transfer problems require re-using previously optimal decisions for solving new tasks, which suggests the need for learning algorithms that can modify the mechanisms for choos…