Towards Playing Full MOBA Games with Deep Reinforcement Learning

no code implementations • NeurIPS 2020 • Deheng Ye, Guibin Chen, Wen Zhang, Sheng Chen, Bo Yuan, Bo Liu, Jia Chen, Zhao Liu, Fuhao Qiu, Hongsheng Yu, Yinyuting Yin, Bei Shi, Liang Wang, Tengfei Shi, Qiang Fu, Wei Yang, Lanxiao Huang, Wei Liu

However, existing work falls short in handling the raw game complexity caused by the explosion of agent combinations, i. e., lineups, when expanding the hero pool in case that OpenAI's Dota AI limits the play to a pool of only 17 heroes.

Dota 2 reinforcement-learning +1

Supervised Learning Achieves Human-Level Performance in MOBA Games: A Case Study of Honor of Kings

no code implementations • 25 Nov 2020 • Deheng Ye, Guibin Chen, Peilin Zhao, Fuhao Qiu, Bo Yuan, Wen Zhang, Sheng Chen, Mingfei Sun, Xiaoqian Li, Siqin Li, Jing Liang, Zhenjie Lian, Bei Shi, Liang Wang, Tengfei Shi, Qiang Fu, Wei Yang, Lanxiao Huang

Unlike prior attempts, we integrate the macro-strategy and the micromanagement of MOBA-game-playing into neural networks in a supervised and end-to-end manner.

PSF-LO: Parameterized Semantic Features Based Lidar Odometry

no code implementations • 26 Oct 2020 • Guibin Chen, BoSheng Wang, Xiaoliang Wang, Huanjun Deng, Bing Wang, Shuo Zhang

Lidar odometry (LO) is a key technology in numerous reliable and accurate localization and mapping systems of autonomous driving.

Autonomous Driving Motion Estimation +1

3D Lidar Mapping Relative Accuracy Automatic Evaluation Algorithm

no code implementations • 1 Jun 2020 • Guibin Chen, Jiong Deng, Dongze Huang, Shuo Zhang

HD (High Definition) map based on 3D lidar plays a vital role in autonomous vehicle localization, planning, decision-making, perception, etc.

Decision Making Simultaneous Localization and Mapping