Abstract
The Deep Q-Network (DQN) is one of the deep reinforcement learning algorithms, which uses deep neural network structure to estimate the Q-value in Q-learning. In the previous work, we designed and implemented a DQN-based Autonomous Aerial Vehicle (AAV) testbed and proposed a Tabu List Strategy based DQN (TLS-DQN). In this paper, we consider corner environment as a new simulation scenario and carried out simulations for normal DQN and TLS-DQN for mobility control of AAV. Simulation results show that TLS-DQN performs better than normal DQN in the corner environment.
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