Shashank Srivastava, Michael Hirsch
Advisors: Dr. Jan Peters, and Dr. Bernhard Scholkopf, Max Planck Institute for Biological Cybernetics
Contemporary autoguiders for guiding telescope mounts for astronomy are essentially rule-based, and depend on rule-based corrections based on object positions at different time instances. We propose a two variable Q-learning optimization algorithm is proposed for autoguiding a German Equitorial Mount for star-tracking and astrophotography. We formulate the problem as a continuous Markov Decision Process with agent ‘actions’ corresponding to motor movement durations, using the GPUSB controller from Shoestring astronomy. The new approach uses robust star-tracking heuristics and reinforcement learning to learn favourable guiding policies, while incorporating mount speciﬁc behavior and current motion of the mount in deciding new movements. The method is extensively tested on artiﬁcal data using simulations, as also for real tracking. The learning algorithm is seen to converge quickly to an optimum strategy, and error rates using the proposed autoguider are seen to be lower than rule-based approaches.