Brinkmann, GerritGerritBrinkmannBessa, Wallace MoreiraWallace MoreiraBessaDücker, Daniel-AndréDaniel-AndréDückerKreuzer, EdwinEdwinKreuzerSolowjow, EugenEugenSolowjow2019-05-232019-05-232018-09-10IEEE International Conference on Robotics and Automation : 6197-6203 (2018-09-10)http://hdl.handle.net/11420/2691Reinforcement learning (RL) allows robots to solve control tasks through interaction with their environment. In this paper we study a model-based value-function RL approach, which is suitable for computationally limited robots and light embedded systems. We develop a diving agent, which uses the RL algorithm for underwater depth stabilization. Simulations and experiments with the micro diving agent demonstrate its ability to learn the depth stabilization task.IngenieurwissenschaftenConference Paper10.1109/ICRA.2018.8461137Other