Geomagnetic Navigation for AUV based on Deep Reinforcement Learning Algorithm For the problem of geomagnetic navigation in the case of autonomous underwater vehicle (AUV) without a prior geomagnetic information, this paper proposes a geomagnetic navigation algorithm based on deep reinforcement learning. Using the correlation between the geomagnetic parameters and the navigation trajectory, the Deep Q Network is used for choosing the navigation direction by multiple geomagnetic components, and the relative position is estimated by the difference between the geomagnetic parameters of the current position and the target position. Under the influence of the above navigation model, the multiple geomagnetic component gradually approaches the target value with the movement of the aircraft, thereby achieving the navigation purpose. By comparing with the evolutionary algorithm (EA), it is proved that the DQN algorithm have better convergence ability.

Endtoend sensorimotor control problems of AUVs with deep reinforcement learning This paper studies on sensorimotor control problems of Autonomous Underwater Vehicles (AUVs) using deep reinforcement learning. We design an endtoend learning architecture mapping original sensor input to continuous control output without referring to the dynamics of vehicles. To avoid difficult and noisy underwater localization, we implement the learning without knowing the positions of AUVs by proposing novel state encoder and reward shaping strategies. Two distinct underwater tasks, obstacle avoidance with sonar sensor and pipeline following with visual sensor, are simulated to validate the effectiveness of proposed architecture and strategies. For the latter, we test the learned policy on realistic images of underwater pipelines to check its generalization ability.