# Integrating Symbolic RL Planning into a BDI-based Autonomous UAV Framework: System Integration and SIL Validation

arXiv: https://arxiv.org/abs/2508.11890 · HTML: https://mlsanity.com/paper/2508.11890/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods.
- Builds on 5 of its 9 references that are in the map.
- First posted on arXiv 16 Aug 2025.

## Placed in

- Deep reinforcement learning methods / [RL for robot navigation and UAV control](https://mlsanity.com/deeprl/rl-for-robot-navigation-and-uav-control/)

## Builds on (most cited first, 5 of 5)

- [PDDLGym: Gym Environments from PDDL Problems](https://mlsanity.com/paper/2002.06432/) (2020)
- [Generalized Planning With Deep Reinforcement Learning](https://mlsanity.com/paper/2005.02305/) (2020)
- [Neurosymbolic Reinforcement Learning and Planning: A Survey](https://mlsanity.com/paper/2309.01038/) (2023)
- [A Hierarchical Reinforcement Learning Framework for Multi-UAV Combat Using Leader-Follower Strategy](https://mlsanity.com/paper/2501.13132/) (2025)
- [Application of Deep Reinforcement Learning to UAV Swarming for Ground Surveillance](https://mlsanity.com/paper/2501.08655/) (2025)

## Abstract (arXiv)

Modern autonomous drone missions increasingly require software frameworks capable of seamlessly integrating structured symbolic planning with adaptive reinforcement learning (RL). Although traditional rule-based architectures offer robust structured reasoning for drone autonomy, their capabilities fall short in dynamically complex operational environments that require adaptive symbolic planning. Symbolic RL (SRL), using the Planning Domain Definition Language (PDDL), explicitly integrates domain-specific knowledge and operational constraints, significantly improving the reliability and safety of unmanned aerial vehicle (UAV) decision making. In this study, we propose the AMAD-SRL framework, an extended and refined version of the Autonomous Mission Agents for Drones (AMAD) cognitive multi-agent architecture, enhanced with symbolic reinforcement learning for dynamic mission planning and execution. We validated our framework in a Software-in-the-Loop (SIL) environment structured identically to an intended Hardware-In-the-Loop Simulation (HILS) platform, ensuring seamless transition to real hardware. Experimental results demonstrate stable integration and interoperability of modules, successful transitions between BDI-driven and symbolic RL-driven planning phases, and consistent mission performance. Specifically, we evaluate a target acquisition scenario in which the UAV plans a surveillance path followed by a dynamic reentry path to secure the target while avoiding threat zones. In this SIL evaluation, mission efficiency improved by approximately 75% over a coverage-based baseline, measured by travel distance reduction. This study establishes a robust foundation for handling complex UAV missions and discusses directions for further enhancement and validation.