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Control theory provides engineers with a multitude of tools to designcontrollers that manipulate the closed-loop behavior and stability of dynamicalsystems. These methods rely heavily on insights about the mathematical modelgoverning the physical system. However, in complex systems, such as autonomousunderwater vehicles performing the dual objective of path-following andcollision avoidance, decision making becomes non-trivial. We propose a solutionusing state-of-the-art Deep Reinforcement Learning (DRL) techniques, to developautonomous agents capable of achieving this hybrid objective without having \`apriori knowledge about the goal or the environment. Our results demonstrate theviability of DRL in path-following and avoiding collisions toward achievinghuman-level decision making in autonomous vehicle systems within extremeobstacle configurations.

Deep Reinforcement Learning Controller for 3D Path-following and Collision Avoidance by Autonomous Underwater Vehicles