Adaptive low level control of AUVs using Deep reinforcement learning
Published:
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.