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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Proceedings ArticleDOI
Comparison of reinforcement learning algorithms applied to the cart-pole problem
TL;DR: RL algorithms such as temporal-difference, policy-gradient actorcritic, and value-function approximation are compared in this context with the standard linear quadratic regulator solution and a novel approach for integrating RL and swing-up controllers is proposed.
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Reinforcement learning approach to goal-regulation in a self-evolutionary manufacturing system
TL;DR: A goal-regulation mechanism that applies a reinforcement learning approach, which is a principal working mechanism for autonomous goal-formation, is proposed and validated by a simulation study on a production planning problem.
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An artificial intelligence approach for optimizing pumping in sewer systems
TL;DR: In this paper, a fuzzy logic system was developed for the control of sewer pumping stations for energy costs savings, which is part of an ongoing collaborative project between Anglian Water and the University of Sheffield.
Dissertation
Reinforcement Learning in Continuous State- and Action-Space
TL;DR: This thesis investigates methods to select the optimal action when artificial neural networks are used to approximate the value function, through the application of numerical optimization techniques and proposes two novel algorithms which are based on the applications of two alternative action selection methods.
Proceedings ArticleDOI
Using associative content-addressable memories to control robots
TL;DR: In this paper, the use of an associative content-addressable memory to model a robot and the world the robot interacts with is discussed, where the model can be learned by storing experiences in the memory.
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