ATC

Description

An air traffic control simulation where an agent manages arrivals, departures, gate assignments, holding patterns, diversions, and runway configurations during weather disruptions at a realistic hub airport (Metro Hub International, inspired by JFK). The environment features a huge combinatorial action space, hard safety constraints, stochastic weather and go-arounds, and multi-objective rewards spanning throughput, delay reduction, fuel efficiency, passenger connections, and safety margins.

Note: this is a synthetic environment that is majority AI-generated; please test thoroughly before using in an RL pipeline.

Capabilities

Combinatorial scheduling of 150-300 flights across multiple runways and 60 gates
Safety constraint satisfaction (FAA wake turbulence separation, gate ADG compatibility)
Weather adaptation (5 weather conditions affecting capacity from 100% to 25%)
Multi-objective optimization balancing throughput, delays, fuel, connections, and safety
Dynamic runway configuration management based on wind conditions
Ground stop and holding pattern management during severe weather

Compute Requirements

No additional compute requirements.

License

MIT

Tasks

30 total tasks across 10 scenario types:

Scenario	Description	Train	Test
clear_day	Normal traffic, good weather	2	1
morning_fog	IFR conditions clearing to VFR	2	1
thunderstorm	Building thunderstorm with capacity reduction	2	1
snow_event	Snow with LOW_IFR and runway clearing	2	1
wind_shift	Wind direction change requiring config switch	2	1
peak_traffic	Holiday-level traffic volume (250-300 flights)	2	1
compound_wx	Multiple weather types in rapid succession	2	1
cascading_delay	Normal weather but systemic early delays	2	1
runway_closure	Reduced capacity from planned maintenance	2	1
crosswind	Strong crosswinds limiting runway options	2	1

Each task runs for 48 time steps of 5 minutes each (4 hours of simulated operations).

Reward Structure

Dense per-step rewards on each advance_time() call, plus a final normalized episode reward in [0, 1].

Final reward is a weighted combination of five objectives:

Component	Weight	Metric
Throughput	30%	Fraction of flights completed (landed + departed)
Delay Reduction	25%	1 - (avg_delay / 120 min), clamped to [0,1]
Passenger Connections	20%	Fraction of connections preserved
Fuel Efficiency	15%	1 - (excess_fuel / max_excess), clamped
Safety	10%	1.0 if zero violations, degrades by 0.2 per violation

Safety override: Any wake separation violation applies a 0.5x multiplier to the entire final reward.

No LLM grader is used; all rewards are computed algorithmically from simulation state.

Data

No external data files. Flight schedules and weather timelines are generated procedurally from deterministic seeds at module load time. Aircraft performance parameters (fuel burn rates, turnaround times, wake categories) are sourced from FAA/ICAO operational data and embedded in the code.

Tools

8 tools available to the agent:

Tool	Description
`view_status()`	View full airport state: weather, flights by phase, gates, connections
`sequence_flight(flight_id, runway_id)`	Assign a runway to an arriving or departing flight
`assign_gate(flight_id, gate_id)`	Assign a gate to an arriving flight
`hold_flight(flight_id)`	Put a flight in a holding pattern
`divert_flight(flight_id)`	Divert a flight to an alternate airport
`set_runway_config(config_name)`	Change active runway configuration
`issue_ground_stop(duration_minutes)`	Issue a ground stop preventing departures
`advance_time()`	Advance simulation by 5 minutes (returns step reward)
`end_shift()`	End simulation early (returns final reward)

Time Horizon

Multi-turn, open-ended within a 48-step window. The agent makes multiple tool calls per step (view status, sequence flights, assign gates) before advancing time. A typical episode involves 48 advance_time() calls plus action calls per step.

Environment Difficulty

[To be determined after baseline evaluation]

Other Environment Requirements

OpenAI API key required for test_agent.py (the environment itself requires no external API keys)

Safety

This is a simulated environment with no real-world impact.

Parameter Sources

All aviation parameters are sourced from FAA, ICAO, IATA, and manufacturer operational data. Key citations:

Parameter	Value	Source
ADG classifications	Groups I–VI by wingspan	FAA AC 150/5300-13B, Table 1-1
Wake turbulence categories	SUPER/HEAVY/B757/LARGE/SMALL	FAA Order JO 7110.65, Section 5-5-4
Wake separation (time-based)	2–4 min	FAA JO 7110.65 NM values converted at ~140 kt approach speed
Runway lengths	8,400–14,511 ft	FAA Airport/Facility Directory (AirNav KJFK); 04L/22R physical length is 12,079 ft
VFR arrival capacity	~60/hr	FAA JFK Airport Capacity Profile (2014)
Weather capacity multipliers	25%–100%	FAA AC 150/5060-5, Airport Capacity Profiles methodology
Go-around base rate	0.5%	NASA TM-20240008006 (U.S. FY2023 actual: 0.39%)
B737-800 holding fuel burn	40 kg/min (~2,400 kg/hr)	ICAO Engine Emissions Databank; Boeing operational data
Turnaround times	30–150 min	IATA Ground Handling Manual (narrow-body 35–50, wide-body 75–150 min)
Domestic MCT	60 min	IATA SSIM Chapter 8 / Resolution 765; JFK domestic MCT 45–60 min
International MCT	120 min	IATA SSIM Chapter 8; JFK international MCT 120–135 min
Passenger counts	76–525 pax	Manufacturer 2-class configuration data
Fuel critical threshold	15 min	Simulation mechanic (FAA 14 CFR 91.167 requires 45 min IFR reserve)

Simplifications: Time-based wake separation (real ATC uses distance-based radar); discrete 5-minute steps; A380 holding fuel rate (100 kg/min) is conservative vs. cruise (~200 kg/min); JFK slot limit of 81 ops/hr (14 CFR 93 Subpart K) not enforced — simulation uses physical runway capacity.

Citations

@dataset{GRATC,
  author = {General Reasoning Inc. Team},
  title = {ATC: Air Traffic Control Environment for OpenReward},
  year = {2025},
  publisher = {OpenReward},
}