Jingwei Zhang

According to our database, Jingwei Zhang authored at least 16 papers between 2017 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Genie: Generative Interactive Environments.

Jake Bruce, Michael Dennis, Ashley Edwards, Jack Parker-Holder, Yuge Shi, Edward Hughes, Matthew Lai, Aditi Mavalankar, Richie Steigerwald, Chris Apps, Yusuf Aytar, Sarah Bechtle, Feryal M. P. Behbahani, Stephanie Chan, Nicolas Heess, Lucy Gonzalez, Simon Osindero, Sherjil Ozair, Scott E. Reed, Jingwei Zhang, Konrad Zolna, Jeff Clune, Nando de Freitas, Satinder Singh, Tim Rocktäschel

CoRR, 2024

Offline Actor-Critic Reinforcement Learning Scales to Large Models.

Jost Tobias Springenberg, Abbas Abdolmaleki, Jingwei Zhang, Oliver Groth, Michael Bloesch, Thomas Lampe, Philemon Brakel, Sarah Bechtle, Steven Kapturowski, Roland Hafner, Nicolas Heess, Martin A. Riedmiller

CoRR, 2024

2023

Mastering Stacking of Diverse Shapes with Large-Scale Iterative Reinforcement Learning on Real Robots.

Thomas Lampe, Abbas Abdolmaleki, Sarah Bechtle, Sandy H. Huang, Jost Tobias Springenberg, Michael Bloesch, Oliver Groth, Roland Hafner, Tim Hertweck, Michael Neunert, Markus Wulfmeier, Jingwei Zhang, Francesco Nori, Nicolas Heess, Martin A. Riedmiller

CoRR, 2023

Equivariant Data Augmentation for Generalization in Offline Reinforcement Learning.

Cristina Pinneri, Sarah Bechtle, Markus Wulfmeier, Arunkumar Byravan, Jingwei Zhang, William F. Whitney, Martin A. Riedmiller

CoRR, 2023

A Generalist Dynamics Model for Control.

Ingmar Schubert, Jingwei Zhang, Jake Bruce, Sarah Bechtle, Emilio Parisotto, Martin A. Riedmiller, Jost Tobias Springenberg, Arunkumar Byravan, Leonard Hasenclever, Nicolas Heess

CoRR, 2023

Leveraging Jumpy Models for Planning and Fast Learning in Robotic Domains.

Jingwei Zhang, Jost Tobias Springenberg, Arunkumar Byravan, Leonard Hasenclever, Abbas Abdolmaleki, Dushyant Rao, Nicolas Heess, Martin A. Riedmiller

CoRR, 2023

2021

Learning navigation policies with deep reinforcement learning.

Jingwei Zhang

PhD thesis, 2021

2020

Efficiency and Equity are Both Essential: A Generalized Traffic Signal Controller with Deep Reinforcement Learning.

Shengchao Yan, Jingwei Zhang, Daniel Büscher, Wolfram Burgard

Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2020

2019

VR-Goggles for Robots: Real-to-Sim Domain Adaptation for Visual Control.

Jingwei Zhang, Lei Tai, Peng Yun, Yufeng Xiong, Ming Liu, Joschka Boedecker, Wolfram Burgard

IEEE Robotics Autom. Lett., 2019

Scheduled Intrinsic Drive: A Hierarchical Take on Intrinsically Motivated Exploration.

Jingwei Zhang, Niklas Wetzel, Nicolai Dorka, Joschka Boedecker, Wolfram Burgard

CoRR, 2019

2018

Curiosity-driven Exploration for Mapless Navigation with Deep Reinforcement Learning.

Oleksii Zhelo, Jingwei Zhang, Lei Tai, Ming Liu, Wolfram Burgard

CoRR, 2018

VR Goggles for Robots: Real-to-sim Domain Adaptation for Visual Control.

Jingwei Zhang, Lei Tai, Yufeng Xiong, Ming Liu, Joschka Boedecker, Wolfram Burgard

CoRR, 2018

Socially Compliant Navigation Through Raw Depth Inputs with Generative Adversarial Imitation Learning.

Lei Tai, Jingwei Zhang, Ming Liu, Wolfram Burgard

Proceedings of the 2018 IEEE International Conference on Robotics and Automation, 2018

2017

Neural SLAM.

Jingwei Zhang, Lei Tai, Joschka Boedecker, Wolfram Burgard, Ming Liu

CoRR, 2017

Perspectives on Deep Multimodel Robot Learning.

Wolfram Burgard, Abhinav Valada, Noha Radwan, Tayyab Naseer, Jingwei Zhang, Johan Vertens, Oier Mees, Andreas Eitel, Gabriel L. Oliveira

Proceedings of the Robotics Research, The 18th International Symposium, 2017

Deep reinforcement learning with successor features for navigation across similar environments.

Jingwei Zhang, Jost Tobias Springenberg, Joschka Boedecker, Wolfram Burgard

Proceedings of the 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2017