A Tabu list strategy based DQN for AAV mobility in indoor single-path environment: Implementation and performance evaluation

Abstract

The Deep Q-Network (DQN) is one of the key methods in the deep reinforcement learning algorithm, which has a deep neural network structure used to estimate Q-values in Q-learning methods. We have previously implemented a simulation system considering DQN for control of actuator nodes in Wireless Sensor and Actuator Networks (WSANs). In addition, we have designed and implemented a DQN based Autonomous Aerial Vehicle (AAV) testbed. In this paper, we propose and evaluate a Tabu List Strategy based DQN (TLS-DQN) for AAV mobility control. For evaluation, we compared the simulation results of the mobility control of AAV in indoor single-path environment using normal DQN and the TLS-DQN. The simulation results show that the TLS-DQN has a better performance than the normal DQN.