Journal ArticleDOI
Neuronlike adaptive elements that can solve difficult learning control problems
Andrew G. Barto,Richard S. Sutton,Charles W. Anderson +2 more
- Vol. 13, Iss: 5, pp 834-846
TLDR
In this article, a system consisting of two neuron-like adaptive elements can solve a difficult learning control problem, where the task is to balance a pole that is hinged to a movable cart by applying forces to the cart base.Abstract:
It is shown how a system consisting of two neuronlike adaptive elements can solve a difficult learning control problem. The task is to balance a pole that is hinged to a movable cart by applying forces to the cart's base. It is argued that the learning problems faced by adaptive elements that are components of adaptive networks are at least as difficult as this version of the pole-balancing problem. The learning system consists of a single associative search element (ASE) and a single adaptive critic element (ACE). In the course of learning to balance the pole, the ASE constructs associations between input and output by searching under the influence of reinforcement feedback, and the ACE constructs a more informative evaluation function than reinforcement feedback alone can provide. The differences between this approach and other attempts to solve problems using neurolike elements are discussed, as is the relation of this work to classical and instrumental conditioning in animal learning studies and its possible implications for research in the neurosciences.read more
Citations
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Journal ArticleDOI
Recurrent neural-network training by a learning automaton approach for trajectory learning and control system design
M.K. Sudareshan,T.A. Condarcure +1 more
TL;DR: A training approach using concepts from the theory of stochastic learning automata that eliminates the need for computation of gradients and hence affords a very simple implementation, particularly for implementation on low-end platforms such as personal computers is presented.
Book ChapterDOI
To discount or not to discount in reinforcement learning: a case study comparing R learning and Q learning
TL;DR: It is argued for using medians over means as a better distribution-free estimator of average performance, and a simple non-parametric significance test for comparing learning data from two RL techniques is described.
Proceedings ArticleDOI
Neural network based process optimization and control
Donald A. Sofge,David A. White +1 more
TL;DR: The authors present, compare, and contrast several neurocontrol systems for controlling and optimizing the manufacture of thermoplastic composite structures and adaptive-critic-based manufacturing neurocontrol.
Journal ArticleDOI
Tuning of PID controllers for unstable processes based on gain and phase margin specifications: a fuzzy neural approach
Ching-Hung Lee,Ching-Cheng Teng +1 more
TL;DR: This paper presents a PID tuning method for unstable processes using an adaptive-network-based-fuzzy-inference system (ANFIS) for given gain and phase margin (GPM) specifications.
Journal ArticleDOI
Neural net-based robust controller design for brushless DC motor drives
Ahmed Rubaai,R. Kotaru +1 more
TL;DR: The ability of the neuro-controller to "remember" previously trained reference tracks when confronted with an input excitation that is markedly different from what it was trained with is investigated.
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