title	AI Disaster Response Coordinator
emoji	🚨
colorFrom	red
colorTo	yellow
sdk	docker
pinned	false
app_port	8000
tags	openenv

AI Disaster Response Coordinator

The AI Disaster Response Coordinator is an OpenEnv-compliant reinforcement learning environment designed to evaluate agent performance in high-stakes resource allocation and crisis management. This environment simulates the complex decision-making required by emergency operations centers during a large-scale disaster.

Overview

The AI Disaster Response Coordinator is an OpenEnv-compliant reinforcement learning environment designed for evaluating multi-agent and LLM-based coordination in high-stakes crises. It features high-fidelity simulation of priority triage, resource allocation, and dynamic wait-time penalties.

🏆 Final Evaluation Results (Baseline)

Evaluated using model=Qwen2.5-72B-Instruct across three tiers of difficulty.

Difficulty	Task	Score	Result
Easy	Priority Identification	1.000	🚀 Perfect
Medium	Capacity Management	0.933	📈 High Performance
Hard	Strategic Trade-offs	0.720	🛠️ Solid Baseline
TOTAL	Weighted Average	0.884

Key Challenges

• Priority Triage: Locations vary in severity (Low, Medium, High), requiring the agent to identify and address the most critical needs first.
• Resource Constraints: Total rescue capacity per step is limited by the number and type of available vehicles.
• Dynamic Penalties: Delays in response lead to increasing "waiting time" penalties, modeling the deteriorating conditions in real-world crisis scenarios.

Quick Start

from my_env import DisasterAction, DisasterResponseClient
from my_env.models import VehicleAssignment

# Initialize the client
client = DisasterResponseClient(base_url="http://localhost:8000")

# Reset the environment to get the initial observation
result = client.reset()
obs = result.observation
print(f"Total Locations: {len(obs.locations)}")

# Dispatch vehicles to target locations
action = DisasterAction(assignments=[
    VehicleAssignment(vehicle_id="veh_1", location_id="loc_1"),
    VehicleAssignment(vehicle_id="veh_2", location_id="loc_3"),
])

# Execute the step
result = client.step(action)
print(f"Lives Saved This Step: {result.observation.people_saved_this_step}")
print(f"Current Cumulative Reward: {result.reward}")

System Requirements and Setup

Docker Deployment (Recommended)

The environment is fully containerized for consistent evaluation.

# Build the environment image
docker build -t disaster-response-env:latest -f server/Dockerfile .

# Run the environment server
docker run -p 8000:8000 disaster_response-env:latest

Local Development

For development and debugging without Docker:

# Install dependencies using uv
uv sync

# Start the FastAPI server
python -m uvicorn server.app:app --host 0.0.0.0 --port 8000

Inference and Baseline Evaluation

Run the standardized inference script to evaluate an agent across all difficulty tiers.

# Hugging Face token (each teammate uses their own; never commit the real value)
export HF_TOKEN="your_huggingface_token"
export API_BASE_URL="https://router.huggingface.co/v1"
export MODEL_NAME="Qwen/Qwen2.5-72B-Instruct"

# Execute evaluation suite
python inference.py

Technical Specification

Action Model

DisasterAction: Encapsulates a list of discrete vehicle-to-location assignments.

Attribute	Type	Description
assignments	List[VehicleAssignment]	Mapping of specific vehicles to target locations.

VehicleAssignment:

Attribute	Type	Description
vehicle_id	str	Unique identifier for the rescue asset.
location_id	str	Unique identifier for the disaster site.

Observation Model

DisasterObservation: Provides the current global state of the crisis.

Attribute	Type	Description
locations	List[LocationState]	List of sites, their severity, and population status.
vehicles	List[VehicleState]	List of assets, their status, and rescue capacities.
time_step	int	Current progress within the episode runtime.
max_steps	int	Horizontal limit for the simulation.
total_people_saved	int	Cumulative performance metric for mission success.

Reward Dynamics

The environment utilizes a dense reward structure to guide agent learning:

• Rescues: +2.0 base reward per individual saved.
• Severity Multipliers: +3.0 (High) or +1.5 (Medium) additional bonus per person based on site triage.
• Efficiency Penalties:
  • Idle penalty: -1.0 for unassigned vehicles while tasks remain.
  • Wait penalty: -0.3 per step for each location left unserved.
  • Wasted Dispatch: -2.0 for sending vehicles to cleared locations.

Scenarios

The environment features three deterministic scenarios designed to test specific agent capabilities.

Tier 1: Easy (Priority Identification)

• Problem: 2 locations (1 High, 1 Low severity), 1 vehicle.
• Objective: Demonstrate basic triage by prioritizing the high-severity location.

Tier 2: Medium (Capacity Management)

• Problem: 4 locations across three severity levels, 2 vehicles with differing capacities.
• Objective: Optimize throughput by matching vehicle capacity to location population.

Tier 3: Hard (Strategic Trade-offs)

• Problem: 6 locations (3 High, 2 Medium, 1 Low), 3 vehicles with limited capacity.
• Objective: Balance conflicting priorities under a tight step limit (20 steps).

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🛠️ Submission Checklist

• OpenEnv V1.0 Compliance: PASS (openenv validate)
• Standardized Inference: PASS (follows stdout protocol)
• Multi-Tier Evaluation: Easy, Medium, and Hard scenarios fully implemented.
• Dockerized Deployment: Fully containerized environment for consistent evaluation.

Built with ❤️ for the Meta Hackathon RL by the Disaster Response Team.