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
More filters
Proceedings Article
Verifiable Reinforcement Learning via Policy Extraction
TL;DR: VIPER, an algorithm that combines ideas from model compression and imitation learning to learn decision tree policies guided by a DNN policy and its Q-function, is proposed and it is shown that it substantially outperforms two baselines.
Journal ArticleDOI
Reinforcement learning and optimal adaptive control: An overview and implementation examples
TL;DR: An example of an implementation of a novel model-free Q-learning based discrete optimal adaptive controller for a humanoid robot arm that uses a novel adaptive dynamic programming (ADP) reinforcement learning (RL) approach to develop an optimal policy on-line.
Journal ArticleDOI
Reinforcement Learning Using a Continuous Time Actor-Critic Framework with Spiking Neurons
TL;DR: In simulations, this model can solve a Morris water-maze-like navigation task, in a number of trials consistent with reported animal performance, and the analytically derived learning rule is consistent with experimental evidence for dopamine-modulated spike-timing-dependent plasticity.
Journal ArticleDOI
Reinforcement Learning – Overview of recent progress and implications for process control
TL;DR: This paper provides an introduction to Reinforcement Learning technology, summarizes recent developments in this area, and discusses their potential implications for the field of process control, and more generally, of operational decision-making.
Journal ArticleDOI
Experimental results on the centipede game in normal form: an investigation on learning
Rosemarie Nagel,Fang Fang Tang +1 more
TL;DR: There is a significant difference in behavior from period to period whether a player has decided to split the pie before or after the opponent, and behavior is structure according to a simple cognitive process, called learning direction theory.
References
More filters
Journal ArticleDOI
Receptive fields, binocular interaction and functional architecture in the cat's visual cortex
David H. Hubel,Torsten N. Wiesel +1 more
TL;DR: This method is used to examine receptive fields of a more complex type and to make additional observations on binocular interaction and this approach is necessary in order to understand the behaviour of individual cells, but it fails to deal with the problem of the relationship of one cell to its neighbours.
Journal ArticleDOI
A Theory of Cerebellar Cortex
TL;DR: A detailed theory of cerebellar cortex is proposed whose consequence is that the cerebellum learns to perform motor skills and two forms of input—output relation are described, both consistent with the cortical theory.
Journal ArticleDOI
Receptive fields and functional architecture in two nonstriate visual areas (18 and 19) of the cat.
David H. Hubel,Torsten N. Wiesel +1 more
TL;DR: To UNDERSTAND VISION in physiological terms represents a formidable problem for the biologist, and one approach is to stimulate the retina with patterns of light while recording from single cells or fibers at various points along the visual pathway.
Journal ArticleDOI
Toward a modern theory of adaptive networks: Expectation and prediction.
TL;DR: The adaptive element presented learns to increase its response rate in anticipation of increased stimulation, producing a conditioned response before the occurrence of the unconditioned stimulus, and is in strong agreement with the behavioral data regarding the effects of stimulus context.
Journal ArticleDOI
Steps toward Artificial Intelligence
TL;DR: The problems of heuristic programming can be divided into five main areas: Search, Pattern-Recognition, Learning, Planning, and Induction as discussed by the authors, and the most successful heuristic (problem-solving) programs constructed to date.