سال انتشار:
2020
عنوان انگلیسی مقاله:
Fixed-Wing UAVs flocking in continuous spaces: A deep reinforcement learning approach
ترجمه فارسی عنوان مقاله:
پهپادهای ثابت بال در فضاهای مداوم هجوم می آورند: یک رویکرد یادگیری تقویتی عمیق
منبع:
Sciencedirect - Elsevier - Robotics and Autonomous Systems, 131 (2020) 103594. doi:10.1016/j.robot.2020.103594
نویسنده:
Chao Yan, Xiaojia Xiang, Chang Wang
چکیده انگلیسی:
Fixed-Wing UAVs (Unmanned Aerial Vehicles) flocking is still a challenging problem due to the
kinematics complexity and environmental dynamics. In this paper, we solve the leader–followers
flocking problem using a novel deep reinforcement learning algorithm that can generate roll angle
and velocity commands by training an end-to-end controller in continuous state and action spaces.
Specifically, we choose CACLA (Continuous Actor–Critic Learning Automation) as the base algorithm
and we use the multi-layer perceptron to represent both the actor and the critic. Besides, we further
improve the learning efficiency by using the experience replay technique that stores the training
data in the experience memory and samples from the memory as needed. We have compared the
performance of the proposed CACER (Continuous Actor–Critic with Experience Replay) algorithm
with benchmark algorithms such as DDPG and double DQN in numerical simulation, and we have
demonstrated the performance of the learned optimal policy in semi-physical simulation without any
parameter tuning.
Keywords: Fixed-wing UAV | Flocking | Reinforcement learning | Actor–critic
قیمت: رایگان
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