Reinforcement learning allows a self-learning agent to stabilize an unmanned aerial vehicle in uncontrolled flight states. To achieve this, a deep deterministic policy gradient algorithm is applied. Through extensions like experience replay memory, …
One of the most difficult challenges in reinforcement learning is the continuous control of systems in a continuous state and action space. This papers goal is to design and implement a reinforcement learning based airplane autopilot that controls an …
This work presents an approach for learning secure step positions, with the objective that the four-legged robot AMEE can safely walk in rough terrain. The autonomous task to be mastered is to identify features of the surrounding area. The …
We develop a theoretical framework for the problem of learning optimal control. We consider a discounted infinite horizon deterministic control problem in the reinforcement learning context. The main objective is to approximate the optimal value …
Das Training eines auf Reinforcement Learning basierenden Agenten gestaltet sich auf physischer Hardware ressourcen-, personal- und zeitaufwändig, weshalb häufig auf das Trainieren innerhalb von Simulationen zurückgegriffen wird. Diese ko ̈nnen die …