Anurag Ajay | DeepAI

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∙ 09/15/2023

Compositional Foundation Models for Hierarchical Planning

To make effective decisions in novel environments with long-horizon goal...

0 Anurag Ajay, et al. ∙

∙ 07/24/2023

Parallel Q-Learning: Scaling Off-policy Reinforcement Learning under Massively Parallel Simulation

Reinforcement learning is time-consuming for complex tasks due to the ne...

0 Zechu Li, et al. ∙

∙ 02/27/2023

Statistical Learning under Heterogenous Distribution Shift

This paper studies the prediction of a target 𝐳 from a pair of random va...

0 Max Simchowitz, et al. ∙

∙ 11/28/2022

Is Conditional Generative Modeling all you need for Decision-Making?

Recent improvements in conditional generative modeling have made it poss...

0 Anurag Ajay, et al. ∙

∙ 10/06/2022

Distributionally Adaptive Meta Reinforcement Learning

Meta-reinforcement learning algorithms provide a data-driven way to acqu...

0 Anurag Ajay, et al. ∙

∙ 07/05/2022

Offline RL Policies Should be Trained to be Adaptive

Offline RL algorithms must account for the fact that the dataset they ar...

0 Dibya Ghosh, et al. ∙

∙ 10/26/2020

OPAL: Offline Primitive Discovery for Accelerating Offline Reinforcement Learning

Reinforcement learning (RL) has achieved impressive performance in a var...

0 Anurag Ajay, et al. ∙

∙ 09/08/2020

Long-Horizon Prediction and Uncertainty Propagation with Residual Point Contact Learners

The ability to simulate and predict the outcome of contacts is paramount...

0 Nima Fazeli, et al. ∙

∙ 04/13/2019

Combining Physical Simulators and Object-Based Networks for Control

Physics engines play an important role in robot planning and control; ho...

0 Anurag Ajay, et al. ∙

∙ 08/09/2018

Augmenting Physical Simulators with Stochastic Neural Networks: Case Study of Planar Pushing and Bouncing

An efficient, generalizable physical simulator with universal uncertaint...

0 Anurag Ajay, et al. ∙

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