Search Results for author: Jingbin Liu

Found 11 papers, 3 papers with code

Mastering Asymmetrical Multiplayer Game with Multi-Agent Asymmetric-Evolution Reinforcement Learning

no code implementations20 Apr 2023 Chenglu Sun, Yichi Zhang, Yu Zhang, Ziling Lu, Jingbin Liu, Sijia Xu, Weidong Zhang

We propose asymmetric-evolution training (AET), a novel multi-agent reinforcement learning framework that can train multiple kinds of agents simultaneously in AMP game.

Multi-agent Reinforcement Learning reinforcement-learning

MDOE: A Spatiotemporal Event Representation Considering the Magnitude and Density of Events

no code implementations RA-L 2022 Fuqiang Gu, Yong Lee, Yuan Zhuang, You Li, Jingbin Liu, Fangwen Yu, Ruiyuan Li, Chao Chen

Event-based sensors (e. g., DVS cameras) are capable of higher dynamic range, higher temporal resolution, lower time latency, and better power efficiency compared to conventional devices (e. g., RGB cameras).

A Method of Generating Measurable Panoramic Image for Indoor Mobile Measurement System

no code implementations27 Oct 2020 Hao Ma, Jingbin Liu, Zhirong Hu, Hongyu Qiu, Dong Xu, Zemin Wang, Xiaodong Gong, Sheng Yang

This paper designs a technique route to generate high-quality panoramic image with depth information, which involves two critical research hotspots: fusion of LiDAR and image data and image stitching.

Image Stitching

M^3VSNet: Unsupervised Multi-metric Multi-view Stereo Network

1 code implementation30 Apr 2020 Baichuan Huang, Hongwei Yi, Can Huang, Yijia He, Jingbin Liu, Xiao Liu

To improve the robustness and completeness of point cloud reconstruction, we propose a novel multi-metric loss function that combines pixel-wise and feature-wise loss function to learn the inherent constraints from different perspectives of matching correspondences.

Point cloud reconstruction

M^3VSNet: Unsupervised Multi-metric Multi-view Stereo Network

1 code implementation21 Apr 2020 Baichuan Huang, Hongwei Yi, Can Huang, Yijia He, Jingbin Liu, Xiao Liu

To improve the robustness and completeness of point cloud reconstruction, we propose a novel multi-metric loss function that combines pixel-wise and feature-wise loss function to learn the inherent constraints from different perspectives of matching correspondences.

Point cloud reconstruction

Soft Q Network

no code implementations20 Dec 2019 Jingbin Liu, Shuai Liu, Xinyang Gu

Deep Q Network (DQN) is a very successful algorithm, yet the inherent problem of reinforcement learning, i. e. the exploit-explore balance, remains.

Q-Learning

Policy Optimization Reinforcement Learning with Entropy Regularization

no code implementations2 Dec 2019 Jingbin Liu, Xinyang Gu, Shuai Liu

We introduce a local action variance for policy network and find it can work collaboratively with the idea of entropy regularization.

Continuous Control reinforcement-learning +1

Reinforcement learning with world model

no code implementations30 Aug 2019 Jingbin Liu, Xinyang Gu, Shuai Liu

We propose an agent framework that integrates off-policy reinforcement learning with world model learning, so as to embody the important features of intelligence in our algorithm design.

Decision Making reinforcement-learning +1

A Survey of Simultaneous Localization and Mapping

no code implementations24 Aug 2019 Baichuan Huang, Jun Zhao, Jingbin Liu

The paper makes an overview in SLAM including Lidar SLAM, visual SLAM, and their fusion.

Robotics Simultaneous Localization and Mapping

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