2 citations · 4 across the 4 of their papers we have counts for
6 papers
Generative Planning for Temporally Coordinated Exploration in Reinforcement Learning
Haichao Zhang, Wei Xu, Haonan Yu
Standard model-free reinforcement learning algorithms optimize a policy that generates the action to be taken in the current time step in order to maximize expected future return.…
Do You Need the Entropy Reward (in Practice)?
Haonan Yu, Haichao Zhang, Wei Xu
Maximum entropy (MaxEnt) RL maximizes a combination of the original task reward and an entropy reward. It is believed that the regularization imposed by entropy, on both policy imp…
Natural Language for Human-Robot Collaboration: Problems Beyond Language Grounding
Seth Pate, Wei Xu, Ziyi Yang +3
To enable robots to instruct humans in collaborations, we identify several aspects of language processing that are not commonly studied in this context. These include location, pla…
Feature Importance in a Deep Learning Climate Emulator
Wei Xu, Xihaier Luo, Yihui Ren +3
We present a study using a class of post-hoc local explanation methods i.e., feature importance methods for "understanding" a deep learning (DL) emulator of climate. Specifically,…
Super Solutions of the Model RB
Guangyan Zhou, Wei Xu
The concept of super solution is a special type of generalized solutions with certain degree of robustness and stability. In this paper we consider the -super solutions of t…
TAAC: Temporally Abstract Actor-Critic for Continuous Control
Haonan Yu, Wei Xu, Haichao Zhang
We present temporally abstract actor-critic (TAAC), a simple but effective off-policy RL algorithm that incorporates closed-loop temporal abstraction into the actor-critic framewor…