[Paper] Leveraging High-Fidelity Digital Models and Reinforcement Learning for Mission Engineering: A Case Study of Aerial Firefighting Under Perfect Information

Source: arXiv - 2512.20589v1

Overview

The paper presents a mission‑engineering framework that couples high‑fidelity digital twins with reinforcement learning (RL) to automate task allocation and re‑configuration in dynamic, uncertain environments. Using an aerial firefighting scenario as a proof‑of‑concept, the authors show that an RL‑driven coordinator can outperform traditional static planning while delivering more consistent mission outcomes.

Key Contributions

• Digital Mission Model (DMM): A DE‑based, high‑resolution simulation environment that captures the physics of fire spread, aircraft dynamics, and resource constraints.
• MDP Formulation of Mission Tactics: Formalizes the adaptive task‑allocation problem as a Markov Decision Process, enabling systematic policy learning.
• RL Agent with Proximal Policy Optimization (PPO): Trains a policy that maps real‑time mission state (e.g., fire front, aircraft status) to actionable decisions (e.g., which aircraft to dispatch, where to drop retardant).
• Empirical Validation: Demonstrates on a realistic aerial firefighting case study that the RL coordinator improves average mission performance and reduces performance variance compared with baseline heuristics.
• Mission‑Agnostic Blueprint: Provides a reusable pipeline that can be applied to other System‑of‑Systems (SoS) domains such as disaster response, autonomous logistics, or multi‑robot exploration.

Methodology

1. Digital Engineering Infrastructure – Build a high‑fidelity, agent‑based simulator that reproduces the fire environment, aircraft capabilities, and communication constraints.
2. State‑Action Definition – Encode the mission snapshot (fire perimeter, aircraft locations, fuel levels, weather) as the RL state vector. Actions correspond to discrete task‑allocation commands (e.g., “assign aircraft A to sector X”).
3. MDP Construction – Define a reward function that balances mission objectives (area burned, time to containment) against operational costs (fuel consumption, aircraft wear).
4. Policy Learning – Use Proximal Policy Optimization, a stable on‑policy RL algorithm, to iteratively improve the policy by running thousands of simulated missions (“sandbox”).
5. Evaluation – Compare the learned policy against two baselines: (a) a static pre‑planned schedule and (b) a simple reactive rule‑based allocator. Metrics include total burned area, containment time, and performance variance across stochastic fire scenarios.

Results & Findings

• The RL coordinator reduces burned area by ~58 % relative to the static plan and cuts containment time by ~31 %.
• Variability across stochastic fire spreads drops dramatically, indicating a more robust policy.
• Ablation studies show that the high‑fidelity simulation is crucial; training on a coarse model leads to a 15 % performance drop.

Practical Implications

• Dynamic Asset Management: Fire departments, disaster‑response agencies, or logistics firms can plug their own digital twins into the pipeline to obtain adaptive dispatch policies without hand‑crafting heuristics.
• Rapid Prototyping: Engineers can iterate on aircraft/fleet designs in the simulator, instantly seeing how changes affect mission success under the learned policy.
• Scalable to Other SoS: The same MDP + PPO approach can be reused for autonomous drone swarms, maritime search‑and‑rescue, or smart grid load balancing, where the environment is partially observable and highly stochastic.
• Reduced Human Burden: Operators receive decision recommendations that already account for future state evolution, freeing them to focus on high‑level supervision rather than minute‑by‑minute allocation.
• Integration Path: The framework can be wrapped as a microservice exposing a REST API; existing command‑and‑control software can query the service for “next best action” given the current mission snapshot.

Limitations & Future Work

• Perfect‑Information Assumption: The study assumes full observability of fire dynamics and aircraft status; real‑world sensor gaps could degrade policy performance.
• Simulation‑Reality Gap: Transferability to live operations hinges on how faithfully the digital twin models physics and communication delays. Domain‑randomization or sim‑to‑real techniques were not explored.
• Scalability to Larger Fleets: Experiments used a modest fleet (3–4 aircraft). Scaling to dozens of heterogeneous assets may require hierarchical RL or multi‑agent coordination mechanisms.
• Explainability: The PPO policy is a black‑box neural network; operators may demand interpretable rationale for critical safety decisions.

Future research directions include incorporating partial observability (POMDPs), online learning during live missions, and extending the framework to multi‑objective optimization (e.g., balancing cost, safety, and environmental impact).

Authors

• İbrahim Oğuz Çetinkaya
• Sajad Khodadadian
• Taylan G. Topçu

Paper Information

• arXiv ID: 2512.20589v1
• Categories: cs.CY, cs.AI, eess.SY, math.OC
• Published: December 23, 2025
• PDF: Download PDF