# A Survey on Reinforcement Learning in Aviation Applications

arXiv: https://arxiv.org/abs/2211.02147 · HTML: https://mlsanity.com/paper/2211.02147/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 38 of its 157 references that are in the map.
- First posted on arXiv 3 Nov 2022.

## Builds on (most cited first, 30 of 38)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Trust Region Policy Optimization](https://mlsanity.com/paper/1502.05477/) (2015)
- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [Deep Reinforcement Learning with Double Q-learning](https://mlsanity.com/paper/1509.06461/) (2015)
- [World Models](https://mlsanity.com/paper/1803.10122/) (2018)
- [Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model](https://mlsanity.com/paper/1911.08265/) (2019)
- [Dueling Network Architectures for Deep Reinforcement Learning](https://mlsanity.com/paper/1511.06581/) (2015)
- [Counterfactual Multi-Agent Policy Gradients](https://mlsanity.com/paper/1705.08926/) (2017)
- [Deep Reinforcement Learning for Autonomous Driving: A Survey](https://mlsanity.com/paper/2002.00444/) (2020)
- [Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms](https://mlsanity.com/paper/1911.10635/) (2019)
- [Learning to reinforcement learn](https://mlsanity.com/paper/1611.05763/) (2016)
- [Neural Network Dynamics for Model-Based Deep Reinforcement Learning with Model-Free Fine-Tuning](https://mlsanity.com/paper/1708.02596/) (2017)
- [Mean Field Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1802.05438/) (2018)
- [Weighted QMIX: Expanding Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2006.10800/) (2020)
- [Continuous Deep Q-Learning with Model-based Acceleration](https://mlsanity.com/paper/1603.00748/) (2016)
- [Model-Based Value Estimation for Efficient Model-Free Reinforcement Learning](https://mlsanity.com/paper/1803.00101/) (2018)
- [Deep Decentralized Multi-task Multi-Agent Reinforcement Learning under Partial Observability](https://mlsanity.com/paper/1703.06182/) (2017)
- [Imagination-Augmented Agents for Deep Reinforcement Learning](https://mlsanity.com/paper/1707.06203/) (2017)
- [Finite-Time Analysis of Asynchronous Stochastic Approximation and Q-Learning](https://mlsanity.com/paper/2002.00260/) (2020)
- [Distributional Soft Actor-Critic: Off-Policy Reinforcement Learning for Addressing Value Estimation Errors](https://mlsanity.com/paper/2001.02811/) (2020)
- [Reinforcement Learning for UAV Attitude Control](https://mlsanity.com/paper/1804.04154/) (2018)
- [Deep Reinforcement Learning with Enhanced Safety for Autonomous Highway Driving](https://mlsanity.com/paper/1910.12905/) (2019)
- [Deep Reinforcement Learning Attitude Control of Fixed-Wing UAVs Using Proximal Policy Optimization](https://mlsanity.com/paper/1911.05478/) (2019)
- [Runtime Safety Assurance Using Reinforcement Learning](https://mlsanity.com/paper/2010.10618/) (2020)
- [Safety Enhancement for Deep Reinforcement Learning in Autonomous Separation Assurance](https://mlsanity.com/paper/2105.02331/) (2021)
- [Optimizing Collision Avoidance in Dense Airspace using Deep Reinforcement Learning](https://mlsanity.com/paper/1912.10146/) (2019)
- [A Deep Multi-Agent Reinforcement Learning Approach to Autonomous Separation Assurance](https://mlsanity.com/paper/2003.08353/) (2020)

## Built on by (most cited first, 19 of 19)

- [Risk-Aware Reinforcement Learning through Optimal Transport Theory](https://mlsanity.com/paper/2309.06239/) (2023)
- [Integrated Conflict Management for UAM with Strategic Demand Capacity Balancing and Learning-based Tactical Deconfliction](https://mlsanity.com/paper/2305.10556/) (2023)
- [Optimal Transport-Assisted Risk-Sensitive Q-Learning](https://mlsanity.com/paper/2406.11774/) (2024)
- [Wasserstein Adaptive Value Estimation for Actor-Critic Reinforcement Learning](https://mlsanity.com/paper/2501.10605/) (2025)
- [Joint Learning of Policy with Unknown Temporal Constraints for Safe Reinforcement Learning](https://mlsanity.com/paper/2305.00576/) (2023)
- [Concurrent Learning of Policy and Unknown Safety Constraints in Reinforcement Learning](https://mlsanity.com/paper/2402.15893/) (2024)
- [Deep deterministic policy gradient with symmetric data augmentation for lateral attitude tracking control of a fixed-wing aircraft](https://mlsanity.com/paper/2407.11077/) (2024)
- [Behavior Preference Regression for Offline Reinforcement Learning](https://mlsanity.com/paper/2503.00930/) (2025)
- [Curriculum-Adapted Robust Reinforcement Learning for UAV Deconfliction in Adversarial Environments](https://mlsanity.com/paper/2506.21129/) (2025)
- [Towards Theoretical Understanding of Data-Driven Policy Refinement](https://mlsanity.com/paper/2305.06796/) (2023)
- [Learning Interpretable Models of Aircraft Handling Behaviour by Reinforcement Learning from Human Feedback](https://mlsanity.com/paper/2305.16924/) (2023)
- [Delay-Aware Reinforcement Learning for Highway On-Ramp Merging under Stochastic Communication Latency](https://mlsanity.com/paper/2403.11852/) (2024)
- [Tradeoffs When Considering Deep Reinforcement Learning for Contingency Management in Advanced Air Mobility](https://mlsanity.com/paper/2407.00197/) (2024)
- [Trading Devil RL: Backdoor attack via Stock market, Bayesian Optimization and Reinforcement Learning](https://mlsanity.com/paper/2412.17908/) (2024)
- [APF+: Boosting adaptive-potential function reinforcement learning methods with a W-shaped network for high-dimensional games](https://mlsanity.com/paper/2503.13557/) (2025)
- [Reinforcement Twinning for Hybrid Control of Flapping-Wing Drones](https://mlsanity.com/paper/2505.18201/) (2025)
- [Safe and Economical UAV Trajectory Planning in Low-Altitude Airspace: A Hybrid DRL-LLM Approach with Compliance Awareness](https://mlsanity.com/paper/2506.08532/) (2025)
- [Smart Commander: A Hierarchical Reinforcement Learning Framework for Fleet-Level PHM Decision Optimization](https://mlsanity.com/paper/2604.07171/) (2026)
- [Flow-Corrected Thompson Sampling for Non-Stationary Contextual Bandits](https://mlsanity.com/paper/2606.23933/) (2026)

## Abstract (arXiv)

Compared with model-based control and optimization methods, reinforcement learning (RL) provides a data-driven, learning-based framework to formulate and solve sequential decision-making problems. The RL framework has become promising due to largely improved data availability and computing power in the aviation industry. Many aviation-based applications can be formulated or treated as sequential decision-making problems. Some of them are offline planning problems, while others need to be solved online and are safety-critical. In this survey paper, we first describe standard RL formulations and solutions. Then we survey the landscape of existing RL-based applications in aviation. Finally, we summarize the paper, identify the technical gaps, and suggest future directions of RL research in aviation.