Abstract
The Deep Q-Network (DQN) is a method of deep reinforcement learning algorithm. DQN is a deep neural network structure used for the estimation of Q value of the Q-learning technique. The authors have previously developed a simulation system on DQN-based behavioral control methods for actuator nodes in Wireless Sensor Actor Networks (WSANs). In this paper, an Autonomous Aerial Vehicle (AAV) testbed is designed and implemented for DQN-based mobility control. We evaluate the performance of the AAV testbed for a indoor single-path environment. For simulation results show that the DQN can control the AAV.
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Acknowledgement
This work was supported by Grant for Promotion of Okayama University of Science (OUS) Research Project (OUS-RP-20-3).
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Saito, N., Oda, T., Hirata, A., Hirota, Y., Hirota, M., Katayama, K. (2021). Design and Implementation of a DQN Based AAV. In: Barolli, L., Takizawa, M., Enokido, T., Chen, HC., Matsuo, K. (eds) Advances on Broad-Band Wireless Computing, Communication and Applications. BWCCA 2020. Lecture Notes in Networks and Systems, vol 159. Springer, Cham. https://doi.org/10.1007/978-3-030-61108-8_32
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DOI: https://doi.org/10.1007/978-3-030-61108-8_32
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