Reinforcement Networks for Collaborative Multi-Agent RL
Published:Dec 28, 2025 10:56
•1 min read
•ArXiv
Analysis
This paper introduces Reinforcement Networks, a novel framework for collaborative Multi-Agent Reinforcement Learning (MARL). It addresses the challenge of end-to-end training of complex multi-agent systems by organizing agents as vertices in a directed acyclic graph (DAG). This approach offers flexibility in credit assignment and scalable coordination, avoiding limitations of existing MARL methods. The paper's significance lies in its potential to unify hierarchical, modular, and graph-structured views of MARL, paving the way for designing and training more complex multi-agent systems.
Key Takeaways
- •Introduces Reinforcement Networks, a novel MARL framework.
- •Organizes agents as a DAG for flexible credit assignment and scalable coordination.
- •Unifies hierarchical, modular, and graph-structured views of MARL.
- •Demonstrates improved performance over standard MARL baselines.
- •Opens a path for designing and training complex multi-agent systems.
Reference
“Reinforcement Networks unify hierarchical, modular, and graph-structured views of MARL, opening a principled path toward designing and training complex multi-agent systems.”