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cs.AI2026
Interference-Aware K-Step Reachable Communication in Multi-Agent Reinforcement Learning
Ziyu Cheng, Jinsheng Ren, Zhouxian Jiang +4
Effective communication is pivotal for addressing complex collaborative tasks in multi-agent reinforcement learning (MARL). Yet, limited communication bandwidth and dynamic, intric…
cs.AI2025
Neural Algorithmic Reasoners informed Large Language Model for Multi-Agent Path Finding
Pu Feng, Size Wang, Yuhong Cao +3
The development and application of large language models (LLM) have demonstrated that foundational models can be utilized to solve a wide array of tasks. However, their performance…
cs.AI2024★ 1 cited
Hierarchical Consensus-Based Multi-Agent Reinforcement Learning for Multi-Robot Cooperation Tasks
Pu Feng, Junkang Liang, Size Wang +6
In multi-agent reinforcement learning (MARL), the Centralized Training with Decentralized Execution (CTDE) framework is pivotal but struggles due to a gap: global state guidance in…