2017
Data-efficient control policy search using residual dynamics learning
IROS 2017poster
In this work, we propose a model-based and data efficient approach for reinforcement learning. The main idea of our algorithm is to combine simulated and real rollouts to efficiently find an optimal control policy. While performing rollouts on the robot, we exploit sensory data to learn a probabilis…