Robust Deterministic Policy Gradient for Disturbance Attenuation and Its Application to Quadrotor Control
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866918229232844800 |
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| author | Lee, Taeho Lee, Donghwan |
| author_facet | Lee, Taeho Lee, Donghwan |
| contents | This paper presents a robust reinforcement learning algorithm called robust deterministic policy gradient (RDPG), which reformulates the H-infinity control problem as a two-player zero-sum dynamic game between a user and an adversary. The method combines deterministic policy gradients with deep reinforcement learning to train a robust policy that attenuates disturbances efficiently. A practical variant, robust deep deterministic policy gradient (RDDPG), integrates twin-delayed updates for stability and sample efficiency. Experiments on an unmanned aerial vehicle demonstrate superior robustness and tracking accuracy under severe disturbance conditions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_21057 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Robust Deterministic Policy Gradient for Disturbance Attenuation and Its Application to Quadrotor Control Lee, Taeho Lee, Donghwan Robotics Artificial Intelligence This paper presents a robust reinforcement learning algorithm called robust deterministic policy gradient (RDPG), which reformulates the H-infinity control problem as a two-player zero-sum dynamic game between a user and an adversary. The method combines deterministic policy gradients with deep reinforcement learning to train a robust policy that attenuates disturbances efficiently. A practical variant, robust deep deterministic policy gradient (RDDPG), integrates twin-delayed updates for stability and sample efficiency. Experiments on an unmanned aerial vehicle demonstrate superior robustness and tracking accuracy under severe disturbance conditions. |
| title | Robust Deterministic Policy Gradient for Disturbance Attenuation and Its Application to Quadrotor Control |
| topic | Robotics Artificial Intelligence |
| url | https://arxiv.org/abs/2502.21057 |