5 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…
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…
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…
Reinforcement Learning with Intrinsically Motivated Feedback Graph for Lost-sales Inventory Control
Zifan Liu, Xinran Li, Shibo Chen +3
Reinforcement learning (RL) has proven to be well-performed and general-purpose in the inventory control (IC). However, further improvement of RL algorithms in the IC domain is imp…