activity
20182023
most citedDeep Reinforcement Learning for Intelligent Transportation Systems

31 citations · 86 across the 21 of their papers we have counts for

collaborators
Showing 2021Show all

7 papers · 1 filter

cs.DC2021

Reinforced Workload Distribution Fairness

Zhiyuan Yao, Zihan Ding, Thomas Heide Clausen

Network load balancers are central components in data centers, that distributes workloads across multiple servers and thereby contribute to offering scalable services. However, whe…

cs.RO2021★ 1 cited

Not Only Domain Randomization: Universal Policy with Embedding System Identification

Zihan Ding

Domain randomization (DR) cannot provide optimal policies for adapting the learning agent to the dynamics of the environment, although it can generalize sub-optimal policies to wor…

cs.CV2021★ 3 cited

Collaborative Spatial-Temporal Modeling for Language-Queried Video Actor Segmentation

Tianrui Hui, Shaofei Huang, Si Liu +5

Language-queried video actor segmentation aims to predict the pixel-level mask of the actor which performs the actions described by a natural language query in the target frames. E…

cs.LG2021★ 3 cited

Probabilistic Mixture-of-Experts for Efficient Deep Reinforcement Learning

Jie Ren, Yewen Li, Zihan Ding +2

Deep reinforcement learning (DRL) has successfully solved various problems recently, typically with a unimodal policy representation. However, grasping distinguishable skills for s…

cs.RO2021

DMotion: Robotic Visuomotor Control with Unsupervised Forward Model Learned from Videos

Haoqi Yuan, Ruihai Wu, Andrew Zhao +3

Learning an accurate model of the environment is essential for model-based control tasks. Existing methods in robotic visuomotor control usually learn from data with heavily labell…

cs.RO2021★ 3 cited

Sim-to-Real Transfer for Robotic Manipulation with Tactile Sensory

Zihan Ding, Ya-Yen Tsai, Wang Wei Lee +1

Reinforcement Learning (RL) methods have been widely applied for robotic manipulations via sim-to-real transfer, typically with proprioceptive and visual information. However, the…

cs.RO2021★ 1 cited

DROID: Minimizing the Reality Gap using Single-Shot Human Demonstration

Ya-Yen Tsai, Hui Xu, Zihan Ding +3

Reinforcement learning (RL) has demonstrated great success in the past several years. However, most of the scenarios focus on simulated environments. One of the main challenges of…