Andrew G. Barto

Associative search network: A reinforcement learning associative memory

Introduction to Reinforcement Learning

From the Publisher: In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. The...

Toward a modern theory of adaptive networks: Expectation and prediction.

Many adaptive neural network theories are based on neuronlike adaptive elements that can behave as single unit analogs of associative conditioning. In this article we develop a ...

A Model of How the Basal Ganglia Generate and Use Neural Signals That Predict Reinforcement

This chapter contains sections titled: Introduction, Dopamine Neurons, Organization of Strtosomal Modules, Mechanism of Responsiveness to Predictors of Reinforcement, Correspond...

On the Computational Economics of Reinforcement Learning

Improving Elevator Performance Using Reinforcement Learning

This paper describes the application of reinforcement learning (RL) to the difficult real world problem of elevator dispatching. The elevator domain poses a combination of chall...

Pattern-recognizing stochastic learning automata

A class of learning tasks is described that combines aspects of learning automation tasks and supervised learning pattern-classification tasks. These tasks are called associativ...