Year: 2003
Type: article
Abstract: We present a provably near-optimal algorithm for reinforcement learn-ing in Markov decision processes in which there is a natural metric on the state space that allows the construction of accurate local models. Our algorithm is a generalization of the E3 algorithm of Kearns and Singh, and assumes a black box for approximate planning. Unlike the original E 3, our algorithm finds a near optimal policy in an amount of time that does not directly depend on the size of the state space, but instead de-pends on the covering numbers of the state space, which are informally the number of neighborhoods in the state space required for accurate lo-cal modeling at a chosen resolution. 1 Introduction, Motivation
Authors: Sham M. Kakade, Michael Kearns, John Langford
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W1747856733