Ranchod, PRosman, Benjamin SKonidaris, G2015-11-162015-11-162015-10Ranchod, P, Rosman, B.S. and Konidaris, G. 2015. Nonparametric bayesian reward segmentation for skill discovery using inverse reinforcement learning. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, Hamburg Germany, September-October 2015http://irl.cs.duke.edu/pubs/npbrs.pdfhttp://hdl.handle.net/10204/8290IEEE/RSJ International Conference on Intelligent Robots and Systems, Hamburg Germany, September-October 2015.We present a method for segmenting a set of unstructured demonstration trajectories to discover reusable skills using inverse reinforcement learning (IRL). Each skill is characterised by a latent reward function which the demonstrator is assumed to be optimizing. The skill boundaries and the number of skills making up each demonstration are unknown. We use a Bayesian nonparametric approach to propose skill segmentations and maximum entropy inverse reinforcement learning to infer reward functions from the segments. This method produces a set of Markov Decision Processes (MDPs) that best describe the input trajectories. We evaluate this approach in a car driving domain and a simulated quadcopter obstacle course, showing that it is able to recover demonstrated skills more effectively than existing methods.enInverse reinforcement learningNonparametric bayesian methodsSkill discoveryImitation learningConference PresentationRanchod, P., Rosman, B. S., & Konidaris, G. (2015). Nonparametric bayesian reward segmentation for skill discovery using inverse reinforcement learning. IEEE. http://hdl.handle.net/10204/8290Ranchod, P, Benjamin S Rosman, and G Konidaris. "Nonparametric bayesian reward segmentation for skill discovery using inverse reinforcement learning." (2015): http://hdl.handle.net/10204/8290Ranchod P, Rosman BS, Konidaris G, Nonparametric bayesian reward segmentation for skill discovery using inverse reinforcement learning; IEEE; 2015. http://hdl.handle.net/10204/8290 .TY - Conference Presentation AU - Ranchod, P AU - Rosman, Benjamin S AU - Konidaris, G AB - We present a method for segmenting a set of unstructured demonstration trajectories to discover reusable skills using inverse reinforcement learning (IRL). Each skill is characterised by a latent reward function which the demonstrator is assumed to be optimizing. The skill boundaries and the number of skills making up each demonstration are unknown. We use a Bayesian nonparametric approach to propose skill segmentations and maximum entropy inverse reinforcement learning to infer reward functions from the segments. This method produces a set of Markov Decision Processes (MDPs) that best describe the input trajectories. We evaluate this approach in a car driving domain and a simulated quadcopter obstacle course, showing that it is able to recover demonstrated skills more effectively than existing methods. DA - 2015-10 DB - ResearchSpace DP - CSIR KW - Inverse reinforcement learning KW - Nonparametric bayesian methods KW - Skill discovery KW - Imitation learning LK - https://researchspace.csir.co.za PY - 2015 T1 - Nonparametric bayesian reward segmentation for skill discovery using inverse reinforcement learning TI - Nonparametric bayesian reward segmentation for skill discovery using inverse reinforcement learning UR - http://hdl.handle.net/10204/8290 ER -