9 citations · 19 across the 12 of their papers we have counts for
12 papers
Multi-agent In-context Coordination via Decentralized Memory Retrieval
Tao Jiang, Zichuan Lin, Lihe Li +6
Large transformer models, trained on diverse datasets, have demonstrated impressive few-shot performance on previously unseen tasks without requiring parameter updates. This capabi…
Step-DAD: Semi-Amortized Policy-Based Bayesian Experimental Design
Marcel Hedman, Desi R. Ivanova, Cong Guan +1
We develop a semi-amortized, policy-based, approach to Bayesian experimental design (BED) called Stepwise Deep Adaptive Design (Step-DAD). Like existing, fully amortized, policy-ba…
Stable Continual Reinforcement Learning via Diffusion-based Trajectory Replay
Feng Chen, Fuguang Han, Cong Guan +4
Given the inherent non-stationarity prevalent in real-world applications, continual Reinforcement Learning (RL) aims to equip the agent with the capability to address a series of s…
Quality-Diversity with Limited Resources
Ren-Jian Wang, Ke Xue, Cong Guan +1
Quality-Diversity (QD) algorithms have emerged as a powerful optimization paradigm with the aim of generating a set of high-quality and diverse solutions. To achieve such a challen…
A Survey of Progress on Cooperative Multi-agent Reinforcement Learning in Open Environment
Lei Yuan, Ziqian Zhang, Lihe Li +2
Multi-agent Reinforcement Learning (MARL) has gained wide attention in recent years and has made progress in various fields. Specifically, cooperative MARL focuses on training a te…
Efficient Human-AI Coordination via Preparatory Language-based Convention
Cong Guan, Lichao Zhang, Chunpeng Fan +6
Developing intelligent agents capable of seamless coordination with humans is a critical step towards achieving artificial general intelligence. Existing methods for human-AI coord…
Learning to Coordinate with Anyone
Lei Yuan, Lihe Li, Ziqian Zhang +5
In open multi-agent environments, the agents may encounter unexpected teammates. Classical multi-agent learning approaches train agents that can only coordinate with seen teammates…
Fast Teammate Adaptation in the Presence of Sudden Policy Change
Ziqian Zhang, Lei Yuan, Lihe Li +5
In cooperative multi-agent reinforcement learning (MARL), where an agent coordinates with teammate(s) for a shared goal, it may sustain non-stationary caused by the policy change o…
Robust multi-agent coordination via evolutionary generation of auxiliary adversarial attackers
Lei Yuan, Zi-Qian Zhang, Ke Xue +6
Cooperative multi-agent reinforcement learning (CMARL) has shown to be promising for many real-world applications. Previous works mainly focus on improving coordination ability via…
Multi-agent Continual Coordination via Progressive Task Contextualization
Lei Yuan, Lihe Li, Ziqian Zhang +3
Cooperative Multi-agent Reinforcement Learning (MARL) has attracted significant attention and played the potential for many real-world applications. Previous arts mainly focus on f…
Efficient Communication via Self-supervised Information Aggregation for Online and Offline Multi-agent Reinforcement Learning
Cong Guan, Feng Chen, Lei Yuan +2
Utilizing messages from teammates can improve coordination in cooperative Multi-agent Reinforcement Learning (MARL). Previous works typically combine raw messages of teammates with…
Heterogeneous Multi-agent Zero-Shot Coordination by Coevolution
Ke Xue, Yutong Wang, Cong Guan +5
Generating agents that can achieve zero-shot coordination (ZSC) with unseen partners is a new challenge in cooperative multi-agent reinforcement learning (MARL). Recently, some stu…