Adaptive low level control of AUVs using Deep reinforcement learning
The work done in the article was published in the journal robotics and autonomous systems.
About the article
In this work we developed a deep RL adaptive controller for AUVs based on the well known deep deterministic policy gradient algorithm (DDPG). The proposal was a goal-oriented deep RL architecture, which takes the available raw sensory information as input and gives as output the continuous control actions which are directly the low-level commands for the AUV’s thrusters.
The research was perform at the Ocean Systems Laboratory of the Heriot-Watt University, using Nessie, the autonomous underwater vehicle developed by students and researchers of the university.
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