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
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Generalization of backpropagation with application to a recurrent gas market model
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Myosin-dependent junction remodelling controls planar cell intercalation and axis elongation
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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.
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