docs/reward_design.md

Reward Design — 911 Dispatch Supervisor

Philosophy

The reward function is designed around one principle: life before property, speed before coverage. Every component weight reflects real dispatch priority doctrine.

Components

Component	Weight	What it measures
Response Time	30%	How fast the correct unit reaches the incident
Triage	25%	Whether unit type matches incident type (MEDIC→medical, ENGINE→fire)
Survival	25%	Whether P1 patients survive to resolution
Coverage	12%	Whether city districts have available units nearby
Protocol	8%	Whether dispatch notes use realistic radio phraseology

Safety Gate

If any Priority-1 incident results in zero survival (patient died, or unit never arrived), the total episode reward is hard-capped at 0.2 — regardless of how well the agent performed on all other incidents.

This is not a bug. It reflects real dispatch accountability: no amount of good coverage or fast response on secondary incidents excuses a preventable P1 death.

Partial Progress

Rewards are non-sparse. An agent receives signal every step for:

• Units moving toward incidents (ETA decreasing)
• Correct unit types being dispatched
• Districts maintaining coverage

This means even a weak agent that dispatches randomly receives informative gradient signal, making the environment suitable for both RL training and LLM evaluation.

Difficulty Gradient

Task	Random Score	LLM Expected	Design Intent
single_incident	0.20	0.55–0.75	One decision, one unit — tests basic triage
multi_incident	0.31	0.40–0.60	Competing P1s — tests priority ordering
mass_casualty	~0.28	0.30–0.50	Surprise waves — tests adaptability
shift_surge	~0.25	0.25–0.40	Resource scarcity — tests planning under constraint

The gap between random and LLM scores is the signal this benchmark measures. A model that scores 0.70 on single_incident but 0.25 on shift_surge is demonstrating exactly the capability boundary the environment is designed to expose.