303 citations · 402 across the 15 of their papers we have counts for
15 papers
A Survey on Game Playing Agents and Large Models: Methods, Applications, and Challenges
Xinrun Xu, Yuxin Wang, Chaoyi Xu +4
The swift evolution of Large-scale Models (LMs), either language-focused or multi-modal, has garnered extensive attention in both academy and industry. But despite the surge in int…
Cradle: Empowering Foundation Agents Towards General Computer Control
Weihao Tan, Wentao Zhang, Xinrun Xu +25
Despite the success in specific scenarios, existing foundation agents still struggle to generalize across various virtual scenarios, mainly due to the dramatically different encaps…
Fully Decentralized Cooperative Multi-Agent Reinforcement Learning: A Survey
Jiechuan Jiang, Kefan Su, Zongqing Lu
Cooperative multi-agent reinforcement learning is a powerful tool to solve many real-world cooperative tasks, but restrictions of real-world applications may require training the a…
Learning from Visual Observation via Offline Pretrained State-to-Go Transformer
Bohan Zhou, Ke Li, Jiechuan Jiang +1
Learning from visual observation (LfVO), aiming at recovering policies from only visual observation data, is promising yet a challenging problem. Existing LfVO approaches either on…
Model-Based Decentralized Policy Optimization
Hao Luo, Jiechuan Jiang, Zongqing Lu
Decentralized policy optimization has been commonly used in cooperative multi-agent tasks. However, since all agents are updating their policies simultaneously, from the perspectiv…
Best Possible Q-Learning
Jiechuan Jiang, Zongqing Lu
Fully decentralized learning, where the global information, i.e., the actions of other agents, is inaccessible, is a fundamental challenge in cooperative multi-agent reinforcement…
MA2QL: A Minimalist Approach to Fully Decentralized Multi-Agent Reinforcement Learning
Kefan Su, Siyuan Zhou, Jiechuan Jiang +3
Decentralized learning has shown great promise for cooperative multi-agent reinforcement learning (MARL). However, non-stationarity remains a significant challenge in fully decentr…
Towards Human-Level Bimanual Dexterous Manipulation with Reinforcement Learning
Yuanpei Chen, Tianhao Wu, Shengjie Wang +8
Achieving human-level dexterity is an important open problem in robotics. However, tasks of dexterous hand manipulation, even at the baby level, are challenging to solve through re…
Model-Based Opponent Modeling
Xiaopeng Yu, Jiechuan Jiang, Wanpeng Zhang +2
When one agent interacts with a multi-agent environment, it is challenging to deal with various opponents unseen before. Modeling the behaviors, goals, or beliefs of opponents coul…
Offline Decentralized Multi-Agent Reinforcement Learning
Jiechuan Jiang, Zongqing Lu
In many real-world multi-agent cooperative tasks, due to high cost and risk, agents cannot continuously interact with the environment and collect experiences during learning, but h…
The Emergence of Individuality
Jiechuan Jiang, Zongqing Lu
Individuality is essential in human society, which induces the division of labor and thus improves the efficiency and productivity. Similarly, it should also be the key to multi-ag…
Learning Fairness in Multi-Agent Systems
Jiechuan Jiang, Zongqing Lu
Fairness is essential for human society, contributing to stability and productivity. Similarly, fairness is also the key for many multi-agent systems. Taking fairness into multi-ag…
Generative Exploration and Exploitation
Jiechuan Jiang, Zongqing Lu
Sparse reward is one of the biggest challenges in reinforcement learning (RL). In this paper, we propose a novel method called Generative Exploration and Exploitation (GENE) to ove…
Graph Convolutional Reinforcement Learning
Jiechuan Jiang, Chen Dun, Tiejun Huang +1
Learning to cooperate is crucially important in multi-agent environments. The key is to understand the mutual interplay between agents. However, multi-agent environments are highly…
Learning Attentional Communication for Multi-Agent Cooperation
Jiechuan Jiang, Zongqing Lu
Communication could potentially be an effective way for multi-agent cooperation. However, information sharing among all agents or in predefined communication architectures that exi…