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End-to-end 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 end-to-end 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.