Acquisition of dynamic control knowledge for a robotic manipulator
Proceedings of the seventh international conference (1990) on Machine learning
Using expectation-maximization for reinforcement learning
Neural Computation
Locally Weighted Learning for Control
Artificial Intelligence Review - Special issue on lazy learning
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Neural Networks for Modelling and Control of Dynamic Systems: A Practitioner's Handbook
Neural Networks for Modelling and Control of Dynamic Systems: A Practitioner's Handbook
Scalable Techniques from Nonparametric Statistics for Real Time Robot Learning
Applied Intelligence
Statistical Learning for Humanoid Robots
Autonomous Robots
Learning to Predict by the Methods of Temporal Differences
Machine Learning
Efficient Non-Linear Control by Combining Q-learning with Local Linear Controllers
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Adaptive Approximation Based Control: Unifying Neural, Fuzzy and Traditional Adaptive Approximation Approaches (Adaptive and Learning Systems for Signal Processing, Communications and Control Series)
Probabilistic inference for solving discrete and continuous state Markov Decision Processes
ICML '06 Proceedings of the 23rd international conference on Machine learning
Learning to Control in Operational Space
International Journal of Robotics Research
State inference in variational bayesian nonlinear state-space models
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Survey Constrained model predictive control: Stability and optimality
Automatica (Journal of IFAC)
Realisation and estimation of piecewise-linear output-error models
Automatica (Journal of IFAC)
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This paper studies the identification and model predictive control in nonlinear hidden state-space models. Nonlinearities are modelled with neural networks and system identification is done with variational Bayesian learning. In addition to the robustness of control, the stochastic approach allows for various control schemes, including combinations of direct and indirect controls, as well as using probabilistic inference for control. We study the noise-robustness, speed, and accuracy of three different control schemes as well as the effect of changing horizon lengths and initialisation methods using a simulated cart-pole system. The simulations indicate that the proposed method is able to find a representation of the system state that makes control easier especially under high noise.