IROS 2017: Hybrid control trajectory optimization under uncertainty

IROS 2017: Hybrid control trajectory optimization under uncertainty

Trajectory optimization is a fundamental problem in robotics. While optimization of continuous control trajectories is well developed, many applications require both discrete and continuous, i.e….

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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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ICML 2016: Model-Free Trajectory Optimization for Reinforcement Learning

ICML 2016: Model-Free Trajectory Optimization for Reinforcement Learning

Many of the recent Trajectory Optimization algorithms alternate between local approximation of the dynamics and conservative policy update. However, linearly approximating the dynamics in order to derive…

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