b'Zongqing Lu'

research

∙ 04/17/2023

LLA-FLOW: A Lightweight Local Aggregation on Cost Volume for Optical Flow Estimation

Lack of texture often causes ambiguity in matching, and handling this is...

0 Jiawei Xu, et al. ∙

research

∙ 03/29/2023

Plan4MC: Skill Reinforcement Learning and Planning for Open-World Minecraft Tasks

We study building a multi-task agent in Minecraft. Without human demonst...

0 Haoqi Yuan, et al. ∙

research

∙ 03/19/2023

CLIP4MC: An RL-Friendly Vision-Language Model for Minecraft

One of the essential missions in the AI research community is to build a...

0 Ziluo Ding, et al. ∙

research

∙ 02/16/2023

Model-Based Decentralized Policy Optimization

Decentralized policy optimization has been commonly used in cooperative ...

0 Hao Luo, et al. ∙

research

∙ 02/16/2023

Learning Multi-Object Positional Relationships via Emergent Communication

The study of emergent communication has been dedicated to interactive ar...

0 Yicheng Feng, et al. ∙

research

∙ 02/02/2023

Best Possible Q-Learning

Fully decentralized learning, where the global information, i.e., the ac...

0 Jiechuan Jiang, et al. ∙

research

∙ 11/06/2022

Decentralized Policy Optimization

The study of decentralized learning or independent learning in cooperati...

0 Kefan Su, et al. ∙

research

∙ 10/25/2022

Entity Divider with Language Grounding in Multi-Agent Reinforcement Learning

We investigate the use of natural language to drive the generalization o...

0 Ziluo Ding, et al. ∙

research

∙ 10/17/2022

Multi-Agent Automated Machine Learning

In this paper, we propose multi-agent automated machine learning (MA2ML)...

2 Zhaozhi Wang, et al. ∙

research

∙ 10/09/2022

State Advantage Weighting for Offline RL

We present state advantage weighting for offline reinforcement learning ...

0 Jiafei Lyu, et al. ∙

research

∙ 09/26/2022

Multi-Agent Sequential Decision-Making via Communication

Communication helps agents to obtain information about others so that be...

15 Ziluo Ding, et al. ∙

research

∙ 09/26/2022

More Centralized Training, Still Decentralized Execution: Multi-Agent Conditional Policy Factorization

In cooperative multi-agent reinforcement learning (MARL), combining valu...

0 Jiangxing Wang, et al. ∙

research

∙ 09/17/2022

MA2QL: A Minimalist Approach to Fully Decentralized Multi-Agent Reinforcement Learning

Decentralized learning has shown great promise for cooperative multi-age...

0 Kefan Su, et al. ∙

research

∙ 06/21/2022

Robust Task Representations for Offline Meta-Reinforcement Learning via Contrastive Learning

We study offline meta-reinforcement learning, a practical reinforcement ...

0 Haoqi Yuan, et al. ∙

research

∙ 06/16/2022

Double Check Your State Before Trusting It: Confidence-Aware Bidirectional Offline Model-Based Imagination

The learned policy of model-free offline reinforcement learning (RL) met...

0 Jiafei Lyu, et al. ∙

research

∙ 06/09/2022

Mildly Conservative Q-Learning for Offline Reinforcement Learning

Offline reinforcement learning (RL) defines the task of learning from a ...

0 Jiafei Lyu, et al. ∙

research

∙ 12/16/2021

Learning to Share in Multi-Agent Reinforcement Learning

In this paper, we study the problem of networked multi-agent reinforceme...

0 Yuxuan Yi, et al. ∙

research

∙ 11/24/2021

APANet: Adaptive Prototypes Alignment Network for Few-Shot Semantic Segmentation

Few-shot semantic segmentation aims to segment novel-class objects in a ...

4 Jiacheng Chen, et al. ∙

research

∙ 10/01/2021

Divergence-Regularized Multi-Agent Actor-Critic

Entropy regularization is a popular method in reinforcement learning (RL...

0 Kefan Su, et al. ∙

research

∙ 08/04/2021

Model-Based Opponent Modeling

When one agent interacts with a multi-agent environment, it is challengi...

0 Xiaopeng Yu, et al. ∙

research

∙ 08/04/2021

Offline Decentralized Multi-Agent Reinforcement Learning

In many real-world multi-agent cooperative tasks, due to high cost and r...

0 Jiechuan Jiang, et al. ∙

research

∙ 06/10/2021

Informative Policy Representations in Multi-Agent Reinforcement Learning via Joint-Action Distributions

In multi-agent reinforcement learning, the inherent non-stationarity of ...

0 Yifan Yu, et al. ∙

research

∙ 04/19/2021

SCNet: Enhancing Few-Shot Semantic Segmentation by Self-Contrastive Background Prototypes

Few-shot semantic segmentation aims to segment novel-class objects in a ...

0 Jiacheng Chen, et al. ∙

research

∙ 02/05/2021

Revisiting Prioritized Experience Replay: A Value Perspective

Experience replay enables off-policy reinforcement learning (RL) agents ...

0 Ang A. Li, et al. ∙

research

∙ 01/04/2021

Variationally and Intrinsically motivated reinforcement learning for decentralized traffic signal control

One of the biggest challenges in multi-agent reinforcement learning is c...

0 Liwen Zhu, et al. ∙

research

∙ 06/11/2020

Learning Individually Inferred Communication for Multi-Agent Cooperation

Communication lays the foundation for human cooperation. It is also cruc...

0 Ziluo Ding, et al. ∙

research

∙ 06/10/2020

The Emergence of Individuality in Multi-Agent Reinforcement Learning

Individuality is essential in human society, which induces the division ...

0 Jiechuan Jiang, et al. ∙

research

∙ 05/18/2020

Niose-Sampling Cross Entropy Loss: Improving Disparity Regression Via Cost Volume Aware Regularizer

Recent end-to-end deep neural networks for disparity regression have ach...

7 Yang Chen, et al. ∙

research

∙ 02/27/2020

XSepConv: Extremely Separated Convolution

Depthwise convolution has gradually become an indispensable operation fo...

0 Jiarong Chen, et al. ∙

research

∙ 10/31/2019

Learning Fairness in Multi-Agent Systems

Fairness is essential for human society, contributing to stability and p...

0 Jiechuan Jiang, et al. ∙

research

∙ 07/30/2019

Towards More Accurate Automatic Sleep Staging via Deep Transfer Learning

Although large annotated sleep databases are publicly available, and mig...

5 Oliver Y. Chén, et al. ∙

research

∙ 04/21/2019

Generative Exploration and Exploitation

Sparse reward is one of the biggest challenges in reinforcement learning...

0 Jiechuan Jiang, et al. ∙

research

∙ 10/22/2018

Graph Convolutional Reinforcement Learning for Multi-Agent Cooperation

Learning to cooperate is crucially important in multi-agent reinforcemen...

0 Jiechuan Jiang, et al. ∙

research

∙ 09/03/2018

Optical Flow Super-Resolution Based on Image Guidence Using Convolutional Neural Network

The convolutional neural network model for optical flow estimation usual...

0 Liping Zhang, et al. ∙

research

∙ 05/20/2018

Learning Attentional Communication for Multi-Agent Cooperation

Communication could potentially be an effective way for multi-agent coop...

0 Jiechuan Jiang, et al. ∙

research

∙ 09/27/2017

Modeling the Resource Requirements of Convolutional Neural Networks on Mobile Devices

Convolutional Neural Networks (CNNs) have revolutionized the research in...

0 Zongqing Lu, et al. ∙

Zongqing Lu

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