Title: Teaching an LLM to Triage Disasters: Building an RL Environment with OpenEnv
Tags: openenv reinforcement-learning disaster-response grpo qwen
markdown# Teaching an LLM to Triage Disasters 🚨
The Problem
During a natural disaster, Emergency Operations Centers (EOCs) are overwhelmed
by thousands of frantic incident reports. A flooded neighborhood, a chemical
plant fire, a hospital wing collapse — all arriving simultaneously. Human
coordinators must instantly decide: which team? what priority? what action?
We built an AI agent that does exactly this.
The Environment
We built Disaster Response Coordination OpenEnv — a multi-step RL environment
where an AI agent acts as an Emergency Incident Commander.
15 real-world scenarios across 3 difficulty tiers, modeled after actual disasters:
🌊 2018 Kerala Floods → dam spillway overflow, communication blackouts
☠️ 2020 Vizag Gas Leak → chemical plant fire, toxic plume evacuation
⚡ 2012 North India Grid Failure → cold-chain medicine failures, hospital blackouts
Action Space
For every incident ticket, the agent must complete a 4-step workflow:
classify → set_priority → draft_reply → submit_ticket
Dense, partial rewards at every step. No sparse end-of-episode signals.
Difficulty Scaling
Tier
Budget
Scenarios
🟢 Easy
40
Single-team, clear incidents
🟡 Medium
48
Multi-agency, ambiguous
🔴 Hard
55
Cascading mass-casualty + time pressure
Training with GRPO
We trained Qwen2.5-7B-Instruct using GRPO (Group Relative Policy Optimization)
via TRL + Unsloth on a Google Colab T4 GPU.
Setup:
Base model: unsloth/Qwen2.5-7B-Instruct-bnb-4bit
Algorithm: GRPOTrainer (TRL)
LoRA: r=16, 4-bit quantization
Epochs: 3 | Steps: 14
Reward: Live environment feedback via HF Space API
The reward function connected directly to our live HF Space — every training
step sent real incident prompts to the environment and received real rewards back.
Training Reward Curve
reward_curve
What We Discovered: Sparse Reward Collapse
The untrained base model immediately revealed why this environment is hard:
Before training, the model hallucinated invalid outputs:
team: "emergency_services" ❌ (not a valid team)
team: "utility repair" ❌
priority: "very-high" ❌ (not a valid priority)
priority: "higher" ❌
After training, the model learned valid action spaces:
team: "rescue" ✅
priority: "urgent" ✅
However, we observed sparse reward collapse — a known RL failure mode where
a small model (7B at 4-bit) struggles to optimize across a multi-step workflow
with interdependent rewards. This validates our environment's quality: it is
genuinely difficult enough to expose real RL failure modes that larger models
or longer training runs would be needed to overcome.
Baseline Results
Agent
Easy
Medium
Hard
Avg
Heuristic Baseline
0.704
0.683
0.660
0.682
GRPO Qwen2.5-7B
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research ongoing
All 3 difficulty tiers passed (score ≥ 0.6).
The Dashboard
We built a military-style tactical command dashboard with:
🗺️ Live OpenStreetMap incident markers with radar pulse animations
⚡ ARIA — AI Incident Analyst (Gemini-powered, analyses any incident live)
📊 Real-time score tracking, threat level bar, team routing
🔔 Operations feed with meaningful event notifications
