Cross-Domain Transfer via Neuroscience-Inspired Meta-Policies in Multi-Agent Systems

While most MARL works (e.g., Huang et al., 2024; Wang et al., 2025) focus on domain-specific learning, this idea draws on neuroscience—specifically, meta-control and executive function theories—to design meta-policies that abstract over domain-specific behaviors. Each agent would have a “neural executive” module that learns to recognize when to switch between, combine, or adapt lower-level policies, much like the prefrontal cortex in humans. Such meta-policies could facilitate rapid transfer: e.g., a team of UAVs trained in package delivery could adapt their coordination strategies to a search-and-rescue domain with minimal retraining. The novelty lies in implementing biologically inspired meta-control within MARL, potentially allowing agents to generalize far beyond the narrow tasks they’re initially trained on—something current frameworks rarely achieve.

References:

1. Multi-Behavior Multi-Agent Reinforcement Learning for Informed Search via Offline Training. Songjun Huang, Chuanneng Sun, Ruo-Qian Wang, D. Pompili (2024). 2024 20th International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT).
2. Distributed Multi-Agent Reinforcement Learning on a Hierarchical Game Model for Railway Engineering Data Collaborative Edge Caching. Yannan Wang, Zhen Liu, Chong Geng, Yidong Li, Xinyu Liu, Qiang Gao (2025). IEEE transactions on intelligent transportation systems (Print).