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The computation and theory of optimal control
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The article was published on 1970-01-01 and is currently open access. It has received 164 citations till now. The article focuses on the topics: Dual control theory & Optimal control.read more
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Journal ArticleDOI
2008 Special Issue: Reinforcement learning of motor skills with policy gradients
Jan Peters,Stefan Schaal +1 more
TL;DR: This paper examines learning of complex motor skills with human-like limbs, and combines the idea of modular motor control by means of motor primitives as a suitable way to generate parameterized control policies for reinforcement learning with the theory of stochastic policy gradient learning.
Journal ArticleDOI
A Survey of Actor-Critic Reinforcement Learning: Standard and Natural Policy Gradients
TL;DR: The workings of the natural gradient is described, which has made its way into many actor-critic algorithms over the past few years, and a review of several standard and natural actor-Critic algorithms is given.
Journal ArticleDOI
Computational approaches to motor learning by imitation
TL;DR: This paper will primarily emphasize the motor side of imitation, assuming that a perceptual system has already identified important features of a demonstrated movement and created their corresponding spatial information.
Proceedings ArticleDOI
Policy Gradient Methods for Robotics
Jan Peters,Stefan Schaal +1 more
TL;DR: An overview on learning with policy gradient methods for robotics with a strong focus on recent advances in the field is given and how the most recently developed methods can significantly improve learning performance is shown.
Book ChapterDOI
Dynamic Movement Primitives -A Framework for Motor Control in Humans and Humanoid Robotics
TL;DR: The Dynamic Movement Primitives (DMPs) as discussed by the authors are units of action that are formalized as stable nonlinear attractor systems, which are useful for autonomous robotics as they are highly flexible in creating complex rhythmic (e.g., locomotion) and discrete (i.e., a tennis swing) behaviors that can quickly adapt to the inevitable perturbations of a dynamically changing, stochastic environment.