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
Improving the fuzzy system performance by fuzzy system ensemble
TL;DR: A fuzzy system ensemble (FSE) that can improve the system performance in non-linear and complex problems is proposed that combines multiple fuzzy systems with an equal system-error weighting method where the weight constant is inversely proportional to the FS's error.
Journal ArticleDOI
Self-organizing state aggregation for architecture design of Q-learning
TL;DR: This work describes a novel algorithm that integrates an adaptive resonance method (ARM), i.e. an ART-based algorithm with a self-organized design, and a Q-learning algorithm, which functions as a cluster to classify input vectors from the outside world.
Journal ArticleDOI
Distinct prediction errors in mesostriatal circuits of the human brain mediate learning about the values of both states and actions: evidence from high-resolution fMRI.
Jaron T. Colas,Wolfgang M. Pauli,Tobias Larsen,Tobias Larsen,J. Michael Tyszka,John P. O'Doherty +5 more
TL;DR: High-resolution fMRI findings suggest that model-free aspects of reward learning in humans can be explained algorithmically with RL in terms of an actor/critic mechanism operating in parallel with a system for more direct action-value learning.
Journal ArticleDOI
Reinforcement Learning Using the Stochastic Fuzzy Min–Max Neural Network
TL;DR: Experimental results indicate that the employment of the fuzzy min–max neural network as an action selection network in reinforcement learning schemes leads to superior learning performance compared with the traditional approach where the multilayer perceptron is employed.
Book ChapterDOI
Neural network models for selecting hand shapes.
T. Iberall,Andrew H. Fagg +1 more
TL;DR: It is shown that the activation of the units in the middle layer demonstrates an internal representation for an opposition, and the use of neural models allows one to explore the implementation of planning processes, while incorporating experimental results from behavioral, anatomical, and neurophysiological studies.
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