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Docking control of an autonomous underwater vehicle (AUV) is a task that isintegral to achieving persistent long term autonomy. This work explores theapplication of state-of-the-art model-free deep reinforcement learning (DRL)approaches to the task of AUV docking in the continuous domain. We provide adetailed formulation of the reward function, utilized to successfully dock theAUV onto a fixed docking platform. A major contribution that distinguishes ourwork from the previous approaches is the usage of a physics simulator to defineand simulate the underwater environment as well as the DeepLeng AUV. We proposea new reward function formulation for the docking task, incorporating severalcomponents, that outperforms previous reward formulations. We evaluate proximalpolicy optimization (PPO), twin delayed deep deterministic policy gradients(TD3) and soft actor-critic (SAC) in combination with our reward function. Ourevaluation yielded results that conclusively show the TD3 agent to be mostefficient and consistent in terms of docking the AUV, over multiple evaluationruns it achieved a 100% success rate and episode return of 10667.1 +- 688.8. Wealso show how our reward function formulation improves over the state of theart.

Deep Reinforcement Learning for Continuous Docking Control of Autonomous Underwater Vehicles: A Benchmarking Study