Can FDQN improve victim tagging speed?
Factorized Deep Q-Network for Cooperative Multi-Agent Reinforcement Learning in Victim Tagging
This paper explores how to minimize the time it takes for a team of responders (e.g., robots, humans, or a hybrid team) to tag all victims in a mass casualty incident (MCI). It formalizes victim tagging as an optimization problem and evaluates five distributed heuristics alongside a factorized deep Q-network (FDQN) reinforcement learning approach.
For LLM-based multi-agent systems, the key takeaways are: (1) Decentralized decision-making with a shared global state is a viable approach for coordinating multiple agents in complex, uncertain environments like MCIs. (2) Factorizing the Q-function allows for scalable learning by avoiding the combinatorial explosion of joint actions. (3) Action masking, informed by real-world constraints (like an FSM), improves learning efficiency. (4) Agent-to-victim ratios are more critical for learning performance than the absolute size of the environment. (5) While promising for smaller-scale scenarios, FDQN struggles as complexity increases, highlighting ongoing challenges in applying deep RL to large-scale multi-agent problems. This emphasizes the need for more sophisticated communication protocols, coordination strategies, and perhaps hybrid approaches combining learned policies with rule-based systems in future research.