# Deep deterministic policy gradient with symmetric data augmentation for lateral attitude tracking control of a fixed-wing aircraft

arXiv: https://arxiv.org/abs/2407.11077 · HTML: https://mlsanity.com/paper/2407.11077/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in RL for robot locomotion and manipulation, one of 15 directions in Deep reinforcement learning methods.
- Builds on 21 of its 68 references that are in the map.
- First posted on arXiv 13 Jul 2024.

## Placed in

- Deep reinforcement learning methods / [RL for robot locomotion and manipulation](https://mlsanity.com/deeprl/rl-for-robot-locomotion-and-manipulation/)

## Builds on (most cited first, 21 of 21)

- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Lipschitz regularity of deep neural networks: analysis and efficient estimation](https://mlsanity.com/paper/1805.10965/) (2018)
- [Uncertainty-Based Offline Reinforcement Learning with Diversified Q-Ensemble](https://mlsanity.com/paper/2110.01548/) (2021)
- [Incorporating Symmetry into Deep Dynamics Models for Improved Generalization](https://mlsanity.com/paper/2002.03061/) (2020)
- [Lie Point Symmetry Data Augmentation for Neural PDE Solvers](https://mlsanity.com/paper/2202.07643/) (2022)
- [Regularizing Action Policies for Smooth Control with Reinforcement Learning](https://mlsanity.com/paper/2012.06644/) (2020)
- [Breaking the Deadly Triad with a Target Network](https://mlsanity.com/paper/2101.08862/) (2021)
- [Small batch deep reinforcement learning](https://mlsanity.com/paper/2310.03882/) (2023)
- [Koopman Q-learning: Offline Reinforcement Learning via Symmetries of Dynamics](https://mlsanity.com/paper/2111.01365/) (2021)
- [A Survey on Reinforcement Learning in Aviation Applications](https://mlsanity.com/paper/2211.02147/) (2022)
- [Data Augmentation vs. Equivariant Networks: A Theory of Generalization on Dynamics Forecasting](https://mlsanity.com/paper/2206.09450/) (2022)
- [Symmetry Learning for Function Approximation in Reinforcement Learning](https://mlsanity.com/paper/1706.02999/) (2017)
- [Analytical bounds on the local Lipschitz constants of ReLU networks](https://mlsanity.com/paper/2104.14672/) (2021)
- [Data-Efficient Deep Reinforcement Learning for Attitude Control of Fixed-Wing UAVs: Field Experiments](https://mlsanity.com/paper/2111.04153/) (2021)
- [Augmenting learning using symmetry in a biologically-inspired domain](https://mlsanity.com/paper/1910.00528/) (2019)
- [Soft Actor-Critic Deep Reinforcement Learning for Fault Tolerant Flight Control](https://mlsanity.com/paper/2202.09262/) (2022)
- [Equivariant Data Augmentation for Generalization in Offline Reinforcement Learning](https://mlsanity.com/paper/2309.07578/) (2023)
- [Theoretical and Numerical Analysis of Approximate Dynamic Programming with Approximation Errors](https://mlsanity.com/paper/1412.6095/) (2014)
- [Towards More Sample Efficiency in Reinforcement Learning with Data Augmentation](https://mlsanity.com/paper/1910.09959/) (2019)
- [Data Augmentation through Expert-guided Symmetry Detection to Improve Performance in Offline Reinforcement Learning](https://mlsanity.com/paper/2112.09943/) (2021)

## Built on by (most cited first, 1 of 1)

- [Temporally smoothed incremental model-based heuristic dynamic programming for command-filtered cascaded online learning flight control](https://mlsanity.com/paper/2507.04346/) (2025)

## Abstract (arXiv)

The symmetry of dynamical systems can be exploited for state-transition prediction and to facilitate control policy optimization. This paper leverages system symmetry to develop sample-efficient offline reinforcement learning (RL) approaches. Under the symmetry assumption for a Markov Decision Process (MDP), a symmetric data augmentation method is proposed. The augmented samples are integrated into the dataset of Deep Deterministic Policy Gradient (DDPG) to enhance its coverage rate of the state-action space. Furthermore, sample utilization efficiency is improved by introducing a second critic trained on the augmented samples, resulting in a dual-critic structure. The aircraft's model is verified to be symmetric, and flight control simulations demonstrate accelerated policy convergence when augmented samples are employed.