no code implementations • 29 Sep 2021 • Shuxing Li, Jiawei Xu, Chun Yuan, Peng Sun, Zhuobin Zheng, Zhengyou Zhang, Lei Han
We provide comprehensive analysis and experiments to elaborate the effect of each component in affecting the agent performance, and demonstrate that the proposed and adopted techniques are important to achieve superior performance in general end-to-end FPS games.
no code implementations • 1 Jul 2020 • Dongxian Wu, Yisen Wang, Zhuobin Zheng, Shu-Tao Xia
Deep neural networks (DNNs) exhibit great success on many tasks with the help of large-scale well annotated datasets.
2 code implementations • AAAI 2020 • Zhihui Lin, Maomao Li, Zhuobin Zheng, Yangyang Cheng, Chun Yuan
To extract spatial features with both global and local dependencies, we introduce the self-attention mechanism into ConvLSTM.
Ranked #22 on Video Prediction on Moving MNIST
2 code implementations • 20 Jul 2019 • Qing Wang, Jiechao Xiong, Lei Han, Meng Fang, Xinghai Sun, Zhuobin Zheng, Peng Sun, Zhengyou Zhang
We introduce Arena, a toolkit for multi-agent reinforcement learning (MARL) research.
2 code implementations • 2 Apr 2018 • Łukasz Kidziński, Sharada Prasanna Mohanty, Carmichael Ong, Zhewei Huang, Shuchang Zhou, Anton Pechenko, Adam Stelmaszczyk, Piotr Jarosik, Mikhail Pavlov, Sergey Kolesnikov, Sergey Plis, Zhibo Chen, Zhizheng Zhang, Jiale Chen, Jun Shi, Zhuobin Zheng, Chun Yuan, Zhihui Lin, Henryk Michalewski, Piotr Miłoś, Błażej Osiński, Andrew Melnik, Malte Schilling, Helge Ritter, Sean Carroll, Jennifer Hicks, Sergey Levine, Marcel Salathé, Scott Delp
In the NIPS 2017 Learning to Run challenge, participants were tasked with building a controller for a musculoskeletal model to make it run as fast as possible through an obstacle course.