AI Pilots: Training Agents for Safer Skies

Author: Denis Avetisyan

A new reinforcement learning framework, powered by transformer networks, demonstrates robust aircraft separation in both structured and unpredictable airspace environments.

The transformer network’s performance, quantified by <span class="katex-eq" data-katex-display="false">\lambda\lambda</span>-returns and entropy during training, demonstrates that configurations with 11, 22, and 33 encoder layers-each tested with three random seeds-converge with predictable variance, as indicated by the smoothing achieved using an exponential moving average with <span class="katex-eq" data-katex-display="false">\alpha = 0.05</span>. — The transformer network’s performance, quantified by $\lambda\lambda$ -returns and entropy during training, demonstrates that configurations with 11, 22, and 33 encoder layers-each tested with three random seeds-converge with predictable variance, as indicated by the smoothing achieved using an exponential moving average with $\alpha = 0.05$ .

This work showcases a single-layer transformer architecture for multi-agent air traffic control, achieving superior performance and generalization through a carefully crafted reward function and state representation.

Conventional air traffic management relies on rigid schedules, limiting adaptability to the inherent stochasticity of modern airspace operations. This limitation motivates the research presented in ‘Transformer-based Multi-agent Reinforcement Learning for Separation Assurance in Structured and Unstructured Airspaces’, which explores a decentralized, learning-based approach to aircraft separation. We demonstrate that a multi-agent reinforcement learning framework, utilizing a single-layer transformer network and a novel state representation, achieves near-zero mid-air collision rates while maintaining desired flight speeds across diverse airspace configurations. Could this adaptable architecture represent a scalable solution for increasingly complex and dynamic airspace environments?

The Static Sky: Limits of Scheduled Air Traffic

Contemporary air traffic management fundamentally operates on a system of pre-planned schedules, a methodology that introduces inherent limitations when faced with real-world unpredictability. Aircraft are assigned specific departure times and flight paths, optimized under anticipated conditions; however, this rigid structure struggles to accommodate unforeseen events such as adverse weather, mechanical issues, or fluctuating demand. Consequently, deviations from these schedules – even minor ones – can create cascading delays and necessitate reactive, rather than proactive, adjustments by air traffic controllers. This reliance on static planning diminishes the system’s ability to dynamically respond to evolving circumstances, potentially compromising efficiency and increasing the risk of congestion as air travel continues to grow in both volume and complexity.

Current air traffic management systems, while sophisticated, increasingly strain under the pressure of growing flight volumes and unforeseen disruptions. Methods like Time-Based Flow Management (TBFM) and the Traffic Management Advisor (TMA) attempt to maintain orderly flow by assigning pre-calculated arrival times and suggesting optimized routes, but these rely on predictable conditions. Unexpected weather patterns, airport congestion, or even minor technical faults can quickly overwhelm these static schedules, creating bottlenecks and increasing the risk of delays. The core issue is a lack of real-time adaptability; these systems struggle to dynamically re-plan in response to events as they unfold, leading to a reactive, rather than proactive, approach to air traffic control. This inflexibility becomes particularly acute during peak travel times or in regions prone to inclement weather, highlighting the need for more responsive and resilient traffic management solutions.

The inherent rigidity of current air traffic management systems introduces a quantifiable risk of Loss of Separation (LoS) incidents, where aircraft fail to maintain mandated minimum distances. While robust safety measures are in place, increasing flight density and the unpredictability of weather patterns are pushing these systems to their limits. A LoS event, even if swiftly corrected, triggers immediate alerts and potential disruptions, but carries the escalating threat of progressing to a Near Mid-Air Collision (NMAC)-a scenario demanding immediate evasive action and representing a catastrophic failure of the system. Consequently, research and development are increasingly focused on adaptable, real-time traffic management solutions that move beyond pre-defined schedules, aiming to predict and mitigate potential conflicts before they compromise flight safety and operational efficiency.

Rewriting the Rules: Reinforcement Learning for Airspace

Reinforcement Learning (RL) diverges from traditional schedule-based Air Traffic Management (ATM) by employing agents that learn through trial and error within a simulated environment. Unlike pre-defined, static schedules, RL algorithms allow agents to dynamically adapt to changing conditions and optimize for specific objectives, such as minimizing delays or fuel consumption. This learning process involves the agent receiving rewards or penalties based on its actions, enabling it to iteratively refine its policy – a mapping from states to actions – to maximize cumulative reward. The simulated environment provides a safe and cost-effective means to train these agents, allowing for exploration of a vast solution space and evaluation of various control strategies without disrupting live air traffic operations. This contrasts with schedule-based systems which rely on pre-programmed rules and are less adaptable to unforeseen circumstances or complex scenarios.

Multi-Agent Reinforcement Learning (MARL) addresses the complexities of Air Traffic Management (ATM) by enabling the coordinated control of multiple aircraft. Unlike single-agent RL, MARL algorithms allow each aircraft to function as an independent agent, learning to optimize its trajectory while simultaneously considering the actions of other agents within the airspace. This is critical for conflict prevention, as safe separation relies on anticipating and reacting to the movements of neighboring aircraft. Successful MARL implementations require algorithms capable of handling the increased state and action spaces inherent in multi-agent systems, as well as addressing challenges related to non-stationarity and the potential for conflicting reward structures between agents. Coordination mechanisms, such as communication protocols or shared reward functions, are often employed to facilitate cooperative behavior and ensure the overall safety and efficiency of the airspace.

Formalizing the separation assurance problem as a Markov Decision Process (MDP) enables the application of rigorous mathematical analysis and algorithm development for multi-agent reinforcement learning (MARL) in Air Traffic Management (ATM). An MDP defines the environment through states representing aircraft positions and velocities, actions representing control inputs (e.g., heading, speed), transition probabilities dictating state evolution based on actions, and a reward function quantifying separation assurance – typically assigning negative rewards for proximity violations and positive rewards for maintaining safe distances. This framework allows for the definition of a state space $S$ , action space $A$ , transition function $P(s'|s,a)$ , and reward function $R(s,a)$ , which are essential inputs for MARL algorithms. By translating the operational requirements of safe air traffic flow into an MDP, researchers can systematically design, train, and evaluate agents capable of decentralized conflict resolution and optimized trajectory management, while also providing quantifiable metrics for performance assessment and safety validation.

Seeing the Whole Sky: Transformer Networks for Dynamic Airspace

The Transformer architecture’s efficacy in dynamic airspace management stems from its self-attention mechanism, which allows the model to weigh the importance of different aircraft states when predicting potential conflicts. Unlike Recurrent Neural Networks (RNNs) that process sequential data step-by-step, Transformers process the entire sequence of aircraft states in parallel. This parallel processing, combined with the self-attention layers, enables the model to capture long-range dependencies between aircraft, crucial for anticipating conflicts arising from complex trajectories. Specifically, the self-attention mechanism computes a weighted sum of all aircraft states, where the weights represent the relevance of each state to the prediction task. This allows the model to focus on the most critical factors contributing to potential conflicts, such as proximity, relative velocity, and heading, improving the accuracy of conflict prediction compared to models with limited contextual awareness.

The Classifier Token is a novel component employed to improve multi-agent situational awareness within the transformer architecture. This token functions by aggregating information received from multiple ‘intruder’ tokens – each representing a potentially conflicting aircraft – and processing it in relation to the features of the ‘ownship’ aircraft. This conditioning on ‘ownship’ data allows the Classifier Token to prioritize and synthesize relevant threat assessments. The resulting aggregated representation provides a comprehensive, context-aware evaluation of the surrounding airspace, enabling more informed decision-making by the agent controlling the ‘ownship’ and ultimately contributing to conflict avoidance.

Agent training and evaluation were performed using the BlueSky simulator, a platform facilitating the development of Multi-Agent Reinforcement Learning (MARL) policies in both Structured Airspace, characterized by defined routes and air traffic control, and Unstructured Airspace, lacking these constraints. This simulation environment enabled the assessment of the transformer-based MARL approach under varying airspace complexities. Results from the study indicate a near-zero Near Mid-Air Collision (NMAC) rate of 0.002 was achieved utilizing a single-layer transformer architecture, demonstrating the potential for robust performance in dynamic airspace management.

Stable Adaptation: Proximal Policy Optimization for Safety

Proximal Policy Optimization (PPO) is a reinforcement learning algorithm utilized for training the Multi-Agent Reinforcement Learning (MARL) agents due to its ability to maintain stable policy updates. PPO achieves this stability by limiting the policy update step size, preventing excessively large changes that could lead to performance degradation. This is accomplished through a clipped surrogate objective function that penalizes policy updates that deviate too far from the previous policy. Consequently, PPO minimizes the risk of catastrophic performance drops during training, enabling the MARL agents to consistently improve their performance in complex environments without experiencing sudden and significant setbacks.

Generalized Advantage Estimation (GAE) is a technique used in reinforcement learning to improve the stability and accuracy of policy gradient methods. GAE operates by calculating the advantage function, which estimates how much better a particular action is compared to the average action at a given state. Traditional advantage estimation often suffers from high variance, leading to unstable training. GAE addresses this by introducing a parameter, λ, that controls the bias-variance tradeoff. A λ value of 0 corresponds to estimating the advantage using only the immediate reward, while a value of 1 uses the entire discounted return. By interpolating between these extremes, GAE reduces the variance of the advantage estimate without introducing excessive bias, resulting in more reliable policy updates and improved learning performance.

The implementation of Proximal Policy Optimization (PPO) in conjunction with Generalized Advantage Estimation (GAE) yields improved performance in multi-agent reinforcement learning (MARL) for aviation safety. Specifically, a 1-layer encoder network, trained with this combination, demonstrated a Time in Loss of Separation (LoS) of 678.154. Furthermore, a 3-layer encoder network achieved 72% Desired Speed Adherence, indicating a substantial ability to maintain optimal flight parameters and reduce the potential for Near Mid-Air Collisions (NMAC).

The study reveals a preference for streamlined architectures-a single-layer transformer demonstrably outperforming more complex, deeper networks in the critical task of separation assurance. This echoes G.H. Hardy’s sentiment: “A mathematician, like a painter or a poet, is a maker of patterns.” The researchers, in effect, aren’t simply training agents; they are crafting a functional pattern of aerial movement, proving that elegance and efficiency often reside in simplicity. The framework’s success isn’t about brute computational force, but about identifying the core relationships necessary to maintain safe and optimal air traffic flow – a beautifully concise solution to a complex problem, and a testament to the power of well-defined reward functions and state representations.

What’s Next?

The demonstrated efficacy of a single-layer transformer in this complex multi-agent environment prompts a re-evaluation of architectural dogma. The field often equates ‘deeper’ with ‘better’, but this work suggests a different calculus – that representational power isn’t solely a function of layer count. One pauses to consider: is the inherent redundancy in deeper networks masking subtle, critical information within the state space? Future work should rigorously explore the limits of this shallow-but-broad approach, pushing the transformer’s capacity with increasingly dense and dynamic airspace scenarios.

Beyond architectural refinement, the reward function itself warrants further scrutiny. The current design successfully incentivizes separation and speed maintenance, yet it remains a relatively coarse-grained signal. Could a more nuanced reward structure – one that explicitly penalizes near-misses or incentivizes energy-efficient flight paths – unlock even more sophisticated and robust behavior? The system currently reacts to potential collisions; a predictive reward signal might allow for anticipation.

Perhaps the most pressing question lies in generalization. The presented framework performs admirably within the defined simulation parameters, but how readily does it adapt to unforeseen circumstances – a sudden system failure, a rogue aircraft, or entirely novel airspace configurations? It’s tempting to view such anomalies as ‘edge cases,’ but one wonders if they aren’t, in fact, the most revealing indicators of the system’s true understanding – or lack thereof. The true test isn’t flawless performance in a controlled environment, but graceful degradation – and continued safe operation – when everything goes wrong.

Original article: https://arxiv.org/pdf/2601.04401.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

The Static Sky: Limits of Scheduled Air Traffic

Rewriting the Rules: Reinforcement Learning for Airspace

Seeing the Whole Sky: Transformer Networks for Dynamic Airspace

Stable Adaptation: Proximal Policy Optimization for Safety

What’s Next?

See also: