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This is a Quantitative Study study.

October 11, 2025PLoS ONEOpen Access

Reinforcement learning for UAV flight controls: Evaluating continuous space reinforcement learning algorithms for fixed-wing UAVs

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Authors

HKHasan Raza Khanzada AMAdnan Maqsood ABAbdul Basit

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Overview

Comparison of continuous space reinforcement learning algorithms shows improved UAV flight control, implying enhanced stability and adaptability in adverse conditions.

Key Points

Reinforcement learning algorithms outperformed classical PID controllers in flight stability and responsiveness, especially during disturbances.
The Soft Actor-Critic algorithm converged in 400 episodes, achieving steady-state error under 3% to demonstrate its effectiveness.
Five continuous-space reinforcement learning algorithms were compared for their applicability to UAV flight dynamics under uncertain conditions.
The analysis provides insights for selecting suitable reinforcement learning algorithms for modern UAV control systems.

Cite This Study

Khanzada et al. (2025) studied this question.

synapsesocial.com/papers/68e9b1d9ba7d64b6fc132d36 https://doi.org/10.1371/journal.pone.0334219

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October 11, 2025PLoS ONEOpen Access

Reinforcement learning for UAV flight controls: Evaluating continuous space reinforcement learning algorithms for fixed-wing UAVs

View Full Paper

Ask AI

Bookmark

Authors

HKHasan Raza Khanzada AMAdnan Maqsood ABAbdul Basit

Discussion

Member takes

Overview

Comparison of continuous space reinforcement learning algorithms shows improved UAV flight control, implying enhanced stability and adaptability in adverse conditions.

Key Points

Reinforcement learning algorithms outperformed classical PID controllers in flight stability and responsiveness, especially during disturbances.
The Soft Actor-Critic algorithm converged in 400 episodes, achieving steady-state error under 3% to demonstrate its effectiveness.
Five continuous-space reinforcement learning algorithms were compared for their applicability to UAV flight dynamics under uncertain conditions.
The analysis provides insights for selecting suitable reinforcement learning algorithms for modern UAV control systems.

Cite This Study

Khanzada et al. (2025) studied this question.

synapsesocial.com/papers/68e9b1d9ba7d64b6fc132d36 https://doi.org/10.1371/journal.pone.0334219

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