1 citations · 3 across the 11 of their papers we have counts for
5 papers · 1 filter
OPAL: Operator-Programmed Algorithms for Landscape-Aware Black-Box Optimization
Junbo Jacob Lian, Mingyang Yu, Kaichen Ouyang +5
Black-box optimization often relies on evolutionary and swarm algorithms whose performance is highly problem dependent. We view an optimizer as a short program over a small vocabul…
VIL2C: Value-of-Information Aware Low-Latency Communication for Multi-Agent Reinforcement Learning
Qian Zhang, Zhuo Sun, Yao Zhang +3
Inter-agent communication serves as an effective mechanism for enhancing performance in collaborative multi-agent reinforcement learning(MARL) systems. However, the inherent commun…
Learn as Individuals, Evolve as a Team: Multi-agent LLMs Adaptation in Embodied Environments
Xinran Li, Chenjia Bai, Zijian Li +3
Large language models (LLMs) possess extensive knowledge bases and strong reasoning capabilities, making them promising tools for complex, multi-agent planning in embodied environm…
A Generative Model Enhanced Multi-Agent Reinforcement Learning Method for Electric Vehicle Charging Navigation
Tianyang Qi, Shibo Chen, Jun Zhang
With the widespread adoption of electric vehicles (EVs), navigating for EV drivers to select a cost-effective charging station has become an important yet challenging issue due to…
Exponential Topology-enabled Scalable Communication in Multi-agent Reinforcement Learning
Xinran Li, Xiaolu Wang, Chenjia Bai +1
In cooperative multi-agent reinforcement learning (MARL), well-designed communication protocols can effectively facilitate consensus among agents, thereby enhancing task performanc…