Journal
2018 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA)
Volume -, Issue -, Pages 6197-6203Publisher
IEEE COMPUTER SOC
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Funding
- German Research Foundation (DFG) [250508151 (Kr 752/33-1)]
- Alexander von Humboldt Foundation
- Brazilian Coordination for the Improvement of Higher Education Personnel
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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.
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