Autonomous Blimp Control Using Model-Free Reinforcement Learning In A Continuous State And Action Space

In this paper, we present an approach that applies the reinforcement learning principle to the problem of learning height control policies for aerial blimps. In contrast to previous approaches, our method does not require sophisticated hand-tuned models, but rather learns the policy online, which makes the system easily adaptable to changing conditions. The blimp we apply our approach to is a small-scale vehicle equipped with an ultrasound sensor that measures its elevation relative to the ground. The major problem in the context of learning control policies lies in the high-dimensional state-action space that needs to be explored in order to identify the values of all state-action pairs. In this paper, we propose a solution to learning continuous control policies based on the Gaussian process model. In practical experiments carried out on a real robot we demonstrate that the system is able to learn a policy online within a few minutes only.