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cs.AI2026
Closed-Loop Vision-Language Planning for Multi-Agent Coordination
Zhiyuan Li, Wenshuai Zhao, Joni Pajarinen
Cooperative multi-agent reinforcement learning (MARL) struggles with sample efficiency, interpretability, and generalization. While Large Language Models (LLMs) offer powerful plan…
cs.AI2025
Learning Progress Driven Multi-Agent Curriculum
Wenshuai Zhao, Zhiyuan Li, Joni Pajarinen
The number of agents can be an effective curriculum variable for controlling the difficulty of multi-agent reinforcement learning (MARL) tasks. Existing work typically uses manuall…
cs.AI2024
Continuous Monte Carlo Graph Search
Kalle Kujanpää, Amin Babadi, Yi Zhao +3
Online planning is crucial for high performance in many complex sequential decision-making tasks. Monte Carlo Tree Search (MCTS) employs a principled mechanism for trading off expl…