Comparison of RL agents.

<div><p>Flight controls are experiencing a major shift with the integration of reinforcement learning (RL). Recent studies have demonstrated the potential of RL to deliver robust and precise control across diverse applications, including the flight control of fixed-wing unmanned aerial v...

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التفاصيل البيبلوغرافية
المؤلف الرئيسي:	Hasan Raza Khanzada (22404835) (author)
مؤلفون آخرون:	Adnan Maqsood (22404838) (author), Abdul Basit (174463) (author)
منشور في:	2025
الموضوعات:	Cell Biology Biotechnology Ecology Inorganic Chemistry Science Policy Biological Sciences not elsewhere classified critical gap persists classical pid controllers 3 %, offering varying flight conditions suitable rl algorithm space rl algorithms rl algorithms outperformed provide valuable insight comparative analysis reveals uav flight controls evaluated rl algorithms comparative analysis flight control rl agents rl ). analysis aims wing uavs wind gusts uncertain environments uavs ). state error soft actor rigorous evaluation results demonstrate relative strengths reinforcement learning recent studies paper aims major shift leading continuous environmental disturbances deliver robust control pitch best trade 400 episodes
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Comparison of RL agents.

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