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[2602.21680] Hierarchical Lead Critic based Multi-Agent Reinforcement Learning
Summary
This paper presents a novel Hierarchical Lead Critic (HLC) architecture for Multi-Agent Reinforcement Learning (MARL), enhancing coordination among agents through a hierarchical training scheme that improves performance and sample efficiency.
Why It Matters
The research addresses limitations in current MARL approaches by introducing a hierarchical structure that combines local and global learning perspectives. This innovation is crucial for developing more efficient and robust multi-agent systems, which have applications in robotics, gaming, and complex task management.
Key Takeaways
- HLC architecture improves coordination in MARL by using hierarchical training.
- Combining local and global perspectives enhances sample efficiency.
- Experimental results show HLC outperforms traditional single hierarchy methods.
- The approach scales effectively with an increasing number of agents.
- HLC is applicable to cooperative, non-communicative, and partially observable environments.
Computer Science > Machine Learning arXiv:2602.21680 (cs) [Submitted on 25 Feb 2026] Title:Hierarchical Lead Critic based Multi-Agent Reinforcement Learning Authors:David Eckel, Henri Meeß View a PDF of the paper titled Hierarchical Lead Critic based Multi-Agent Reinforcement Learning, by David Eckel and Henri Mee{\ss} View PDF HTML (experimental) Abstract:Cooperative Multi-Agent Reinforcement Learning (MARL) solves complex tasks that require coordination from multiple agents, but is often limited to either local (independent learning) or global (centralized learning) perspectives. In this paper, we introduce a novel sequential training scheme and MARL architecture, which learns from multiple perspectives on different hierarchy levels. We propose the Hierarchical Lead Critic (HLC) - inspired by natural emerging distributions in team structures, where following high-level objectives combines with low-level execution. HLC demonstrates that introducing multiple hierarchies, leveraging local and global perspectives, can lead to improved performance with high sample efficiency and robust policies. Experimental results conducted on cooperative, non-communicative, and partially observable MARL benchmarks demonstrate that HLC outperforms single hierarchy baselines and scales robustly with increasing amounts of agents and difficulty. Comments: Subjects: Machine Learning (cs.LG); Multiagent Systems (cs.MA) Cite as: arXiv:2602.21680 [cs.LG] (or arXiv:2602.21680v1 [cs.LG] for this v...
