Deep deterministic policy gradient with symmetric data augmentation for lateral attitude tracking control of a fixed-wing aircraft
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arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866915979998527488 |
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| author | Li, Yifei van Kampen, Erik-Jan |
| author_facet | Li, Yifei van Kampen, Erik-Jan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_11077 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Deep deterministic policy gradient with symmetric data augmentation for lateral attitude tracking control of a fixed-wing aircraft Li, Yifei van Kampen, Erik-Jan Machine Learning Artificial Intelligence 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. |
| title | Deep deterministic policy gradient with symmetric data augmentation for lateral attitude tracking control of a fixed-wing aircraft |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2407.11077 |