Finding Safe Policies in Model-Based Active Learning

@inproceedings{Martnez2014FindingSP,
  title={Finding Safe Policies in Model-Based Active Learning},
  author={David Martı́nez and Guillem Aleny{\`a} and Carme Torras},
  year={2014}
}
Task learning in robotics is a time-consuming process, and model-based reinforcement learning algorithms have been proposed to learn with just a small amount of experiences. However, reducing the number of experiences used to learn implies that the algorithm may overlook crucial actions required to get an optimal behavior. For example, a robot may learn… CONTINUE READING