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

İbrahim Oğuz Çetinkaya, Sajad Khodadadian, Taylan G. Topcu

TL;DR

The paper tackles ME challenges in SoS by integrating high-fidelity digital mission representations with reinforcement learning to enable adaptive, centralized task coordination. It formulates the firefighting mission as an MDP and uses PPO to train a policy that maps mission states to tactical actions, evaluated in a notional aerial firefighting scenario under perfect information. Results show the RL coordinator surpasses random tactics and reduces performance variability, suggesting that DE-enabled simulations coupled with RL can provide a robust, mission-agnostic framework for ME. The work lays a foundation for extending to fleet design, heterogeneous assets, and imperfect information in future ME practice.

Abstract

As systems engineering (SE) objectives evolve from design and operation of monolithic systems to complex System of Systems (SoS), the discipline of Mission Engineering (ME) has emerged which is increasingly being accepted as a new line of thinking for the SE community. Moreover, mission environments are uncertain, dynamic, and mission outcomes are a direct function of how the mission assets will interact with this environment. This proves static architectures brittle and calls for analytically rigorous approaches for ME. To that end, this paper proposes an intelligent mission coordination methodology that integrates digital mission models with Reinforcement Learning (RL), that specifically addresses the need for adaptive task allocation and reconfiguration. More specifically, we are leveraging a Digital Engineering (DE) based infrastructure that is composed of a high-fidelity digital mission model and agent-based simulation; and then we formulate the mission tactics management problem as a Markov Decision Process (MDP), and employ an RL agent trained via Proximal Policy Optimization. By leveraging the simulation as a sandbox, we map the system states to actions, refining the policy based on realized mission outcomes. The utility of the RL-based intelligent mission coordinator is demonstrated through an aerial firefighting case study. Our findings indicate that the RL-based intelligent mission coordinator not only surpasses baseline performance but also significantly reduces the variability in mission performance. Thus, this study serves as a proof of concept demonstrating that DE-enabled mission simulations combined with advanced analytical tools offer a mission-agnostic framework for improving ME practice; which can be extended to more complicated fleet design and selection problems in the future from a mission-first perspective.

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

TL;DR

The paper tackles ME challenges in SoS by integrating high-fidelity digital mission representations with reinforcement learning to enable adaptive, centralized task coordination. It formulates the firefighting mission as an MDP and uses PPO to train a policy that maps mission states to tactical actions, evaluated in a notional aerial firefighting scenario under perfect information. Results show the RL coordinator surpasses random tactics and reduces performance variability, suggesting that DE-enabled simulations coupled with RL can provide a robust, mission-agnostic framework for ME. The work lays a foundation for extending to fleet design, heterogeneous assets, and imperfect information in future ME practice.

Abstract

As systems engineering (SE) objectives evolve from design and operation of monolithic systems to complex System of Systems (SoS), the discipline of Mission Engineering (ME) has emerged which is increasingly being accepted as a new line of thinking for the SE community. Moreover, mission environments are uncertain, dynamic, and mission outcomes are a direct function of how the mission assets will interact with this environment. This proves static architectures brittle and calls for analytically rigorous approaches for ME. To that end, this paper proposes an intelligent mission coordination methodology that integrates digital mission models with Reinforcement Learning (RL), that specifically addresses the need for adaptive task allocation and reconfiguration. More specifically, we are leveraging a Digital Engineering (DE) based infrastructure that is composed of a high-fidelity digital mission model and agent-based simulation; and then we formulate the mission tactics management problem as a Markov Decision Process (MDP), and employ an RL agent trained via Proximal Policy Optimization. By leveraging the simulation as a sandbox, we map the system states to actions, refining the policy based on realized mission outcomes. The utility of the RL-based intelligent mission coordinator is demonstrated through an aerial firefighting case study. Our findings indicate that the RL-based intelligent mission coordinator not only surpasses baseline performance but also significantly reduces the variability in mission performance. Thus, this study serves as a proof of concept demonstrating that DE-enabled mission simulations combined with advanced analytical tools offer a mission-agnostic framework for improving ME practice; which can be extended to more complicated fleet design and selection problems in the future from a mission-first perspective.
Paper Structure (13 sections, 3 equations, 5 figures, 1 table)

This paper contains 13 sections, 3 equations, 5 figures, 1 table.

Table of Contents

  1. Introduction
  2. Methodology
  3. Firefighting Mission Model
  4. Markov Decision Process Formulation and RL Implementation
  5. Implementation of the RL Agent
  6. Mission Scenario Analyzed in this Paper
  7. State Space
  8. Action Space
  9. Reward
  10. Simulation Workflow for Training the RL Agent
  11. Results
  12. Discussion
  13. Conclusions and Future Work

Figures (5)

  • Figure 1: Methodological Overview
  • Figure 2: Simulation progression, and two example track and suppress operations
  • Figure 3: Operational workflow of the simulation environment for training the RL-based intelligent mission coordination agent
  • Figure 4: Operational workflow of the simulation environment for training the RL-based intelligent mission coordination agent
  • Figure 5: Operational workflow of the simulation environment for training the RL-based intelligent mission coordination agent