Improving the Learning Rate by Inducing a Transition Model

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Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 3 (AAMAS'04)

Improving the Learning Rate by Inducing a Transition Model (PDF)

New York City, New York, USA

July 19-July 23

ISBN: 0-7695-2092-8

	ASCII Text	x
	Robert Bridle, Eric McCreath, "Improving the Learning Rate by Inducing a Transition Model," Autonomous Agents and Multiagent Systems, International Joint Conference on, vol. 3, pp. 1330-1331, Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 3 (AAMAS'04), 2004.

	BibTex	x
	@article{ 10.1109/AAMAS.2004.10293, author = {Robert Bridle and Eric McCreath}, title = {Improving the Learning Rate by Inducing a Transition Model}, journal ={Autonomous Agents and Multiagent Systems, International Joint Conference on}, volume = {3}, year = {2004}, isbn = {0-7695-2092-8}, pages = {1330-1331}, doi = {http://doi.ieeecomputersociety.org/10.1109/AAMAS.2004.10293}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }

	RefWorks Procite/RefMan/Endnote	x
	TY - CONF JO - Autonomous Agents and Multiagent Systems, International Joint Conference on TI - Improving the Learning Rate by Inducing a Transition Model SN - 0-7695-2092-8 SP1330 EP1331 A1 - Robert Bridle, A1 - Eric McCreath, PY - 2004 KW - null VL - 3 JA - Autonomous Agents and Multiagent Systems, International Joint Conference on ER -

Robert Bridle, Australian National University

Eric McCreath, Australian National University

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/AAMAS.2004.10293

In general, a reinforcement learning agent requires many trials in order to find a successful policy in a domain. In this paper we investigate inducing a transition model to reduce the number of trials required by an agent.We discuss an approach that incorporates transition model learning within a contemporary agent design.

Citation:

Robert Bridle, Eric McCreath, "Improving the Learning Rate by Inducing a Transition Model," aamas, vol. 3, pp.1330-1331, Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 3 (AAMAS'04), 2004

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