39 citations · 117 across the 43 of their papers we have counts for
18 papers · 1 filter
RePo: Resilient Model-Based Reinforcement Learning by Regularizing Posterior Predictability
Chuning Zhu, Max Simchowitz, Siri Gadipudi +1
Visual model-based RL methods typically encode image observations into low-dimensional representations in a manner that does not eliminate redundant information. This leaves them s…
Compositional Foundation Models for Hierarchical Planning
Anurag Ajay, Seungwook Han, Yilun Du +7
To make effective decisions in novel environments with long-horizon goals, it is crucial to engage in hierarchical reasoning across spatial and temporal scales. This entails planni…
REBOOT: Reuse Data for Bootstrapping Efficient Real-World Dexterous Manipulation
Zheyuan Hu, Aaron Rovinsky, Jianlan Luo +3
Dexterous manipulation tasks involving contact-rich interactions pose a significant challenge for both model-based control systems and imitation learning algorithms. The complexity…
Maximizing Success Rate of Payment Routing using Non-stationary Bandits
Aayush Chaudhary, Abhinav Rai, Abhishek Gupta
This paper discusses the system architecture design and deployment of non-stationary multi-armed bandit approaches to determine a near-optimal payment routing policy based on the r…
Tackling Combinatorial Distribution Shift: A Matrix Completion Perspective
Max Simchowitz, Abhishek Gupta, Kaiqing Zhang
Obtaining rigorous statistical guarantees for generalization under distribution shift remains an open and active research area. We study a setting we call combinatorial distributio…
Breadcrumbs to the Goal: Goal-Conditioned Exploration from Human-in-the-Loop Feedback
Marcel Torne, Max Balsells, Zihan Wang +4
Exploration and reward specification are fundamental and intertwined challenges for reinforcement learning. Solving sequential decision-making tasks requiring expansive exploration…
Navigating Fairness in Radiology AI: Concepts, Consequences,and Crucial Considerations
Vasantha Kumar Venugopal, Abhishek Gupta, Rohit Takhar +3
Artificial Intelligence (AI) has significantly revolutionized radiology, promising improved patient outcomes and streamlined processes. However, it's critical to ensure the fairnes…
Fighting Uncertainty with Gradients: Offline Reinforcement Learning via Diffusion Score Matching
H. J. Terry Suh, Glen Chou, Hongkai Dai +3
Gradient-based methods enable efficient search capabilities in high dimensions. However, in order to apply them effectively in offline optimization paradigms such as offline Reinfo…
Self-Supervised Reinforcement Learning that Transfers using Random Features
Boyuan Chen, Chuning Zhu, Pulkit Agrawal +2
Model-free reinforcement learning algorithms have exhibited great potential in solving single-task sequential decision-making problems with high-dimensional observations and long h…
Learning to Extrapolate: A Transductive Approach
Aviv Netanyahu, Abhishek Gupta, Max Simchowitz +2
Machine learning systems, especially with overparameterized deep neural networks, can generalize to novel test instances drawn from the same distribution as the training data. Howe…
Guiding Pretraining in Reinforcement Learning with Large Language Models
Yuqing Du, Olivia Watkins, Zihan Wang +5
Reinforcement learning algorithms typically struggle in the absence of a dense, well-shaped reward function. Intrinsically motivated exploration methods address this limitation by…
Dexterous Manipulation from Images: Autonomous Real-World RL via Substep Guidance
Kelvin Xu, Zheyuan Hu, Ria Doshi +4
Complex and contact-rich robotic manipulation tasks, particularly those that involve multi-fingered hands and underactuated object manipulation, present a significant challenge to…
Unpacking Reward Shaping: Understanding the Benefits of Reward Engineering on Sample Complexity
Abhishek Gupta, Aldo Pacchiano, Yuexiang Zhai +2
Reinforcement learning provides an automated framework for learning behaviors from high-level reward specifications, but in practice the choice of reward function can be crucial fo…
Distributionally Adaptive Meta Reinforcement Learning
Anurag Ajay, Abhishek Gupta, Dibya Ghosh +2
Meta-reinforcement learning algorithms provide a data-driven way to acquire policies that quickly adapt to many tasks with varying rewards or dynamics functions. However, learned m…
Teachable Reinforcement Learning via Advice Distillation
Olivia Watkins, Trevor Darrell, Pieter Abbeel +2
Training automated agents to complete complex tasks in interactive environments is challenging: reinforcement learning requires careful hand-engineering of reward functions, imitat…
Combining Varied Learners for Binary Classification using Stacked Generalization
Sruthi Nair, Abhishek Gupta, Raunak Joshi +1
The Machine Learning has various learning algorithms that are better in some or the other aspect when compared with each other but a common error that all algorithms will suffer fr…
Autonomous Reinforcement Learning: Formalism and Benchmarking
Archit Sharma, Kelvin Xu, Nikhil Sardana +4
Reinforcement learning (RL) provides a naturalistic framing for learning through trial and error, which is appealing both because of its simplicity and effectiveness and because of…
Fully Autonomous Real-World Reinforcement Learning with Applications to Mobile Manipulation
Charles Sun, Jędrzej Orbik, Coline Devin +4
We study how robots can autonomously learn skills that require a combination of navigation and grasping. While reinforcement learning in principle provides for automated robotic sk…