303 citations · 402 across the 15 of their papers we have counts for
10 papers · 1 filter
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…
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…