In this project we set up the AMARSi Oncilla Simulator1 and used Dynamic movement primitivies (DMPs) as movement representation and optimized their parameters in a reinforcement learning framework to adapt the robot’s behaviour to new problems. After some experiments on toy examples we applied an open-loop control scheme to the Oncilla Simulator. In the end we want to apply this approach to a real robot, the AMARSi2 Oncilla quadroped and evaluate its performance.