8 papers
Puzzle it Out: Local-to-Global World Model for Offline Multi-Agent Reinforcement Learning
Sijia Li, Xinran Li, Shibo Chen +1
Offline multi-agent reinforcement learning (MARL) aims to solve cooperative decision-making problems in multi-agent systems using pre-collected datasets. Existing offline MARL meth…
GAS: Enhancing Reward-Cost Balance of Generative Model-assisted Offline Safe RL
Zifan Liu, Xinran Li, Shibo Chen +1
Offline Safe Reinforcement Learning (OSRL) aims to learn a policy to achieve high performance in sequential decision-making while satisfying constraints, using only pre-collected d…
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…