Reinforcement Learning I: Introduction

Reinforcement Learning I: Introduction (1998)

by Richard S. Sutton , Andrew G. Barto

Citations:

Datum	Value	Source
TITLE	Reinforcement Learning: An Introduction	INFERENCE
AUTHOR NAME	Richard S. Sutton	SVM HeaderParse 0.1
AUTHOR NAME	Andrew G. Barto	SVM HeaderParse 0.1
ABSTRACT	Introduction Richard S. Sutton and Andrew G. Barto c fl All rights reserved [In which we try to give a basic intuitive sense of what reinforcement learning is and how it differs and relates to other fields, e.g., supervised learning and neural networks, genetic algorithms and artificial life, control theory. Intuitively, RL is trial and error (variation and selection, search) plus learning (association, memory). We argue that RL is the only field that seriously addresses the special features of the problem of learning from interaction to achieve long-term goals.] 1 Learning from Interaction The idea that we learn by interacting with our environment is probably the first to occur to us when we think about the nature of learning. When an infant plays, waves its arms, or looks about, it has no explicit teacher, but it does have a direct sensorimotor connection to its environment. Exercising this connection produces a wealth of infor	user correction - Legacy Corrections
YEAR	1998	user correction - Legacy Corrections
CITATIONS	20 found	ParsCit 1.0