4 citations · 9 across the 4 of their papers we have counts for
4 papers
IQL-TD-MPC: Implicit Q-Learning for Hierarchical Model Predictive Control
Rohan Chitnis, Yingchen Xu, Bobak Hashemi +4
Model-based reinforcement learning (RL) has shown great promise due to its sample efficiency, but still struggles with long-horizon sparse-reward tasks, especially in offline setti…
Burst phase distribution of SGR J1935+2154 based on Insight-HXMT
Xuefeng Lu, Liming Song, Mingyu Ge +10
On April 27, 2020, the soft gamma ray repeater SGR J1935+2154 entered its intense outburst episode again. Insight-HXMT carried out about one month observation of the source. A tota…
Learning General World Models in a Handful of Reward-Free Deployments
Yingchen Xu, Jack Parker-Holder, Aldo Pacchiano +5
Building generally capable agents is a grand challenge for deep reinforcement learning (RL). To approach this challenge practically, we outline two key desiderata: 1) to facilitate…
Lsh-sampling Breaks the Computation Chicken-and-egg Loop in Adaptive Stochastic Gradient Estimation
Beidi Chen, Yingchen Xu, Anshumali Shrivastava
Stochastic Gradient Descent or SGD is the most popular optimization algorithm for large-scale problems. SGD estimates the gradient by uniform sampling with sample size one. There h…