JMLR 2017: Non-parametric Policy Search with Limited Information Loss.

JMLR 2017: Non-parametric Policy Search with Limited Information Loss.

Learning complex control policies from non-linear and redundant sensory input is an important challenge for reinforcement learning algorithms. Non-parametric methods that approximate values functions or transition…

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New JMLR paper accepted: "Non-parametric Policy Search with Limited Information Loss."

New JMLR paper accepted: “Non-parametric Policy Search with Limited Information Loss.”

H. van Hoof, G. Neumann, and J. Peters, “Non-parametric policy search with limited information loss,” Journal of Machine Learning Research, 2018. [BibTeX] [Abstract] [Download PDF]…

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New IJJR Paper accepted! "Learning Movement Primitive Libraries through Probabilistic Segmentation."

New IJJR Paper accepted! “Learning Movement Primitive Libraries through Probabilistic Segmentation.”

R. Lioutikov, G. Neumann, G. Maeda, and J. Peters, “Learning movement primitive libraries through probabilistic segmentation,” International Journal of Robotics Research (IJRR), vol. 36, iss….

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IJJR 2017:  Learning Movement Primitive Libraries through Probabilistic Segmentation

IJJR 2017: Learning Movement Primitive Libraries through Probabilistic Segmentation

Movement primitives are a well established approach for encoding and executing movements. While the primitives themselves have been extensively researched, the concept of movement primitive libraries…

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New ICML Paper: Local Bayesian Optimization

New ICML Paper: Local Bayesian Optimization

R. Akrour, D. Sorokin, J. Peters, and G. Neumann, “Local Bayesian optimization of motor skills,” in International Conference on Machine Learning (ICML), 2017. [BibTeX] [Abstract]…

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ICML 2017: Local Bayesian Optimization

ICML 2017: Local Bayesian Optimization

Bayesian optimization is renowned for its sample efficiency but its application to higher dimensional tasks is impeded by its focus on global optimization. To scale to higher dimensional problems,…

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AURO2017: Using Probabilistic Movement Primitives in Robotics

AURO2017: Using Probabilistic Movement Primitives in Robotics

Movement Primitives are a well-established paradigm for modular movement representation and generation. They provide a data-driven representation of movements and support generalization to novel situations, temporal modulation, sequencing of…

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New AURO Paper: Using Probabilistic Movement Primitives in Robotics

New AURO Paper: Using Probabilistic Movement Primitives in Robotics

Well deserved, Alex! A. Paraschos, C. Daniel, J. Peters, and G. Neumann, “Using probabilistic movement primitives in robotics,” Autonomous Robots, vol. 42, iss. 3, pp….

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GECCO 2017: Deriving and Improving CMA-ES with Information-Geometric Trustregions

GECCO 2017: Deriving and Improving CMA-ES with Information-Geometric Trustregions

CMA-ES is one of the most popular stochastic search algorithms. It performs favourably in many tasks without the need of extensive parameter tuning. The algorithm…

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ICAPS 2017: State-regularized policy search for linearized dynamical systems

ICAPS 2017: State-regularized policy search for linearized dynamical systems

Trajectory-Centric Reinforcement Learning and Trajectory Optimization methods optimize a sequence of feedbackcontrollers by taking advantage of local approximations of model dynamics and cost functions. Stability of the policy…

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