Reinforcement learning of depth stabilization with a micro diving agent

Reinforcement 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.

DDC Class

620: Ingenieurwissenschaften

Projekt(e)

Dezentrale kooperative Exploration von nichtstationären räumlich und zeitlich verteilten Feldern mit autonomen Unterwasserfahrzeugen

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Reinforcement learning of depth stabilization with a micro diving agent