SUMMARY AI summary by gpt-5-mini
Multi-Agent Reinforcement Learning environment that models truck slot booking and landside congestion at Amsterdam Airport Schiphol Cargo Hub. Designed for researchers and engineers studying decentralized coordination between Ground Handling Agents (GHAs), transporters and an optional Orchestrator. Implements a high-fidelity discrete-event simulation (SimPy) enforcing Digital Truck Slot Planning (DTP) rules (45-min slots, frozen window, ANPR gate checks, booking/priority logic). Exposes a PettingZoo interface and is being integrated with BenchMARL for centralized training / decentralized execution (CTDE). Key modules: dtp_platform, objects, demand, infrastructure, kpi_tracker, road, service_time, and schiphol_env. Current status: platform and simulation complete, PettingZoo wrapper done, BenchMARL adapter and multi-scenario training pending.
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| Created on GitHub | 2026-04-26 |
| Last push | 2026-05-09 |
| First seen here | 2026-05-09 |
| Last fetched | 2026-05-09 18:18 |
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