Reinforcement Learning for Fixed-Wing Aircraft

• Reinforcement learning for fixed-wing aircraft is a data-driven approach that synthesizes adaptive control laws using high-fidelity 6-DOF simulation models for guidance and stability.
• RL techniques leverage policy gradient, value-based, and model-based algorithms to improve tracking precision, robustness under uncertainty, and energy efficiency.
• Integrating RL with classical autopilot architectures and domain randomization enables rapid convergence, robust performance, and promising sim-to-real transfer.

Reinforcement learning (RL) for fixed-wing aircraft comprises a set of methodologies in which control laws for guidance, attitude, or higher-level strategy are synthesized via data-driven optimization in simulation or hardware, with the agent learning policies through direct interaction with the six-degree-of-freedom (6-DOF) aircraft dynamics. Typical target applications include autonomous glider guidance, envelope-protected flight, attitude stabilization, energy-efficient trajectory optimization, and robust path-following under uncertainty. RL-based controllers offer potential improvements over classical model-based designs in handling nonlinearity, high-dimensional observations, and unmodeled model variations.

1. Aircraft Modeling and Dynamics

Fixed-wing aircraft RL control tasks universally embed high-fidelity flight dynamics governed by rigid-body 6-DOF equations. The standard modeling scheme is as follows:

• Frames and Variables: Navigation (NED) and body frames, with position $\mathbf{p}=[x_N, y_N, z_N]^\top$, linear velocity $\mathbf{v}^b$, and orientation given by Euler angles $\Theta = [\phi, \theta, \psi]^\top$ or quaternions. Mass $m$ and inertia $I$ are parameterized.
• Equations of Motion: Translational and rotational dynamics comprise

$$m\,\dot{\mathbf{v}}^b = m\,B(\Theta)^\top\mathbf{g} + \mathbf{F}^b_{\rm aero} - \Omega(\boldsymbol{\omega}^b)\,\mathbf{v}^b,$$

$$I\,\dot{\boldsymbol{\omega}}^b = \mathbf{M}^b_{\rm aero} - \boldsymbol{\omega}^b\times (I\,\boldsymbol{\omega}^b).$$

Here $\mathbf{F}^b_{\rm aero}$ and $\mathbf{M}^b_{\rm aero}$ sum lift, drag, and moment contributions from all aerodynamic surfaces, actuators (elevon, aileron, elevator, rudder), and, if present, propulsion.

• Disturbances and Actuator Models: Environmental disturbances are typically injected using Dryden turbulence models for wind and gusts. Actuator dynamics (servo delays, rate limits, faults) are either included as second-order lags or via explicit bias/fault models.
• Simulation Fidelity: High-resolution integration (e.g., RK4 at 100 Hz) is standard. Camera or sensor models (e.g., pinhole camera for vision-based tasks) can be added as needed (Cahn et al., 30 Nov 2025, Olivares et al., 26 Sep 2024).

2. RL Problem Formulation and Task Design

Fixed-wing aircraft RL problems are generally posed as Markov Decision Processes (MDPs) or, if partial observation is relevant, as POMDPs:

• State Representation: State vectors typically include a subset of aircraft attitude, rates, control surface states, and task-specific outputs (e.g., image coordinates in guidance), sometimes stacked over recent time steps to encode history. For guidance tasks, the observation can integrate transformed target positions (e.g., pixel coordinates) (Cahn et al., 30 Nov 2025), while for envelope protection, protected variables (angle-of-attack $\alpha$, load factor $n_z$, rates) are explicit (Catak et al., 8 Jun 2024).
• Action Space: Actions may be normalized and mapped to control surface deflections (e.g., $a_t = [\delta_{el}^*, \delta_{ail}^*]^\top$ with post-processing via physical saturations) or, for hierarchical RL, may parameterize feedback law gains (Shin et al., 2019). For energy-optimization and high-level tasks, actions may comprise entire trajectory parameters or waypoints (Galkin et al., 2021).
• Reward Design: Reward functions are engineered to encode task objectives (tracking, energy, constraint violation). Typical forms:
  • Tracking: Negative norm of deviation from reference (e.g., pixel error, attitude error).
  • Smoothness and Economy: Quadratic penalties on actuator usage or their rates.
  • Safety and Constraints: Strong penalties for violating protected flight envelope variables.
  • Terminal Rewards: Success bonuses upon task completion (e.g., goal reached, safe landing) (Özbek et al., 2022).
• Temporal Structure: Single-shot, episodic, or continuous tasks are supported; episodic duration is often limited to a set number of simulation steps or to task-specific completion criteria.

3. RL Algorithmic Frameworks

A spectrum of RL algorithms has been deployed for fixed-wing aircraft, selected for sample efficiency, stability, and compatibility with continuous, high-dimensional state–action spaces.

• Policy Gradient Methods: Proximal Policy Optimization (PPO) (Cahn et al., 30 Nov 2025, Bøhn et al., 2019) and Soft Actor-Critic (SAC) (Bøhn et al., 2021, Olivares et al., 26 Sep 2024) are widely used, leveraging actor–critic architectures for continuous control.
• Value-Based and Off-Policy Methods: Twin Delayed Deep Deterministic Policy Gradient (TD3) (Ozbek et al., 2022, Özbek et al., 2022), Deep Deterministic Policy Gradient (DDPG) (Shin et al., 2019, Catak et al., 8 Jun 2024) are common for tasks requiring sample reuse/off-policy training.
• Tabular and Fuzzy Q-learning: For low-dimensional tasks, discrete Q-learning, often augmented via fuzzy inference to enable smooth continuous action synthesis, yields high reliability and control smoothness (Zahmatkesh et al., 2023, Zahmatkesh et al., 2022).
• Model-Based RL: Temporal Difference Model Predictive Control (TD-MPC) combines learned latent dynamics with model-predictive-control (MPC) planning, offering robust performance in nonlinear or disturbance-prone regimes (Olivares et al., 26 Sep 2024).
• Adversarial RL: Robust Adversarial Reinforcement Learning (RARL), wherein an adversarial agent actively perturbs aerodynamic model coefficients within rate-bounded limits, has been shown to increase policy resilience to worst-case uncertainties (Marquis et al., 18 Oct 2025).

Methodology	Example Papers	Use Cases
PPO	(Cahn et al., 30 Nov 2025, Bøhn et al., 2019)	Guidance, attitude stabilization
TD3/DDPG	(Ozbek et al., 2022, Shin et al., 2019, Catak et al., 8 Jun 2024)	Path opt., gain tuning, FEP
Fuzzy Q-learning	(Zahmatkesh et al., 2023)	Auto-landing, robust attitude
TD-MPC (Model-based)	(Olivares et al., 26 Sep 2024)	Robust attitude
RARL	(Marquis et al., 18 Oct 2025)	Robust path-following, uncertainty

4. Training Methodologies and Simulation Setup

Training protocols synchronize simulation environments, episode initialization, and domain randomization:

• Parallel Environments: Massive parallelization (e.g., 55 environments) decreases wall-clock time for convergence (Cahn et al., 30 Nov 2025).
• Randomization: Variation in initial states, wind conditions, and aerodynamic parameters supports robustness to real-world uncertainty. Specialized scenarios test transfer and generalization, such as hardware-in-the-loop, field experiments, and domain randomization (Bøhn et al., 2021).
• Simulation Hardware: Typical RL controllers converge within $10^5$–$10^6$ simulation steps (minutes to an hour on consumer GPUs for PPO/SAC).
• Validation: Monte Carlo evaluation across hundreds of randomized episodes quantifies control precision, robustness, and constraint adherence. Integrators (e.g., RK4, 100 Hz) are used to ensure numerical stability.

5. Performance and Comparative Evaluation

RL controllers are benchmarked against tuned classical controllers (PID, gain-scheduled autopilots, dynamic inversion, Riccati/LQR):

• Precision Metrics: RL achieves lower mean miss distances, reduced 2$\sigma$ dispersion, and improved tracking in gusty or turbulent conditions relative to hand-tuned PIDs (Cahn et al., 30 Nov 2025, Bøhn et al., 2019).
• Robustness: RL policies, especially those trained with adversarial perturbations or domain randomization, display enhanced stability and tracking under unmodeled aerodynamic variation, sensor noise, and actuator failures (Marquis et al., 18 Oct 2025, Zahmatkesh et al., 2023).
• Smoothness and Energy: Policy-regularization techniques (action variation penalty, conditioning for smooth action) reduce actuator fluctuations and energy usage, approaching or surpassing classical designs in efficiency (Olivares et al., 26 Sep 2024).
• Sample Efficiency: Approaches that inject domain knowledge (e.g., fixing autopilot structures and learning gains only, reference shaping) accelerate convergence by up to 80% (Shin et al., 2019).
• Sim-to-Real Transfer: Empirical validation in flight tests, with prior simulated training under actuation delay and parameter randomization, has demonstrated that RL controllers can match or exceed state-of-the-art PID autopilots with less than five minutes of real flight data (Bøhn et al., 2021).

6. Advances in RL Design for Fixed-Wing Problems

Several foundational insights and refinements have emerged:

• Structured Observation and Multi-Step Memory: Careful selection of minimal observation vectors (error, rates, recent actions) and stacking for temporal context expedites training while maintaining policy expressivity (Bøhn et al., 2019).
• Policy Generalization: RL policies trained for specific sensors and actuators (e.g., velocity probes in the wake for flow control (Liu et al., 7 May 2025); vision-based target tracking (Cahn et al., 30 Nov 2025)) generalize across 2D–3D tasks and varying spanwise actuation.
• Robustness via Adversarial and Model-Based RL: Adversarial environment perturbations yield controllers that withstand edge-case uncertainties; model-based planning explicitly exploits learned dynamics for better performance in nonlinear or hard-reference regimes (Marquis et al., 18 Oct 2025, Olivares et al., 26 Sep 2024).
• Integration of Classical Structures: Imposing a classical feedback architecture (e.g., three-loop autopilot) and only learning scheduling gains combines RL flexibility with interpretability and guaranteed stability margins (Shin et al., 2019).
• Fuzzy and Interpolated Q-learning: For lower-dimensional fixed-wing control, fuzzy augmentation of Q-tables or weighted assignment smooths action chattering inherent in discrete RL, approaching the continuous-action control afforded by policy-gradient DRL (Zahmatkesh et al., 2022, Zahmatkesh et al., 2023).

7. Practical Applications and Open Challenges

RL is being evaluated and operationalized for a range of fixed-wing roles:

• Guidance and Window-based Navigation: Camera-based LOS guidance has been demonstrated to outperform PID for high-precision terminal glider tasks (Cahn et al., 30 Nov 2025).
• Envelope Protection: DDPG-based RL FEP logic can enforce angle-of-attack, $n_z$, and pitch-rate constraints in real time, smoothly counteracting pilot-induced or external inputs (Catak et al., 8 Jun 2024).
• Auto-landing and Agility: Q-learning with fuzzy aggregation attains reliable thresholds for attitude, height, and control effort in robust landing on unstable airframes (Zahmatkesh et al., 2023).
• Energy-efficient Service Trajectory Planning: Multi-agent DDQN has enabled decentralized, interference-aware energy optimization for networks of fixed-wing access points (Galkin et al., 2021).
• Active Aerodynamic Flow Control: Closed-loop RL flow control of airfoil near-wake structure achieves $>30\%$ increase in lift–drag ratio and generalizes to spanwise-structured 3D actuation (Liu et al., 7 May 2025).

Key open directions include scaling RL to full-mission autonomy (combining guidance, control, and high-level decision-making), synthesizing certifiable and explainable RL policies, minimizing data requirements for real-world adaptation, and ensuring robust constraint satisfaction under extreme disturbances and partial observability. Robustness to severe and nonstationary turbulence, explicit safety guarantees, and integration with real-time onboard computational and avionics constraints remain active research frontiers.