Reinforcement learning under circumstances beyond its control
Gaskett, Chris (2003) Reinforcement learning under circumstances beyond its control. Proceedings of the international conference on computational intelligence, robotics and autonomous systems. Proceedings of the international conference on computational intelligence for modelling control and automation (CIMCA2003) , Vienna, Austria .
|PDF - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader|
Decision theory addresses the task of choosing an action; it provides robust decision-making criteria that support decision-making under conditions of uncertainty or risk. Decision theory has been applied to produce reinforcement learning algorithms that manage uncertainty in state-transitions. However, performance when there is uncertainty regarding the selection of future actions must also be considered, since reinforcement learning tasks are multiple-step decision problems. This work proposes beta-pessimistic Q-learning—a reinforcement learning algorithm that does not assume complete control.
|Item Type:||Conference Item (UNSPECIFIED)|
|Keywords:||Reinforcement learning, Beta-pessimistic, Risk, Uncertainty|
|FoR Codes:||01 MATHEMATICAL SCIENCES > 0102 Applied Mathematics > 010205 Financial Mathematics @ 0%|
17 PSYCHOLOGY AND COGNITIVE SCIENCES > 1702 Cognitive Science > 170203 Knowledge Representation and Machine Learning @ 0%
|Deposited On:||04 Oct 2006|
|Last Modified:||14 Feb 2011 01:23|
Last 12 Months: 66
Repository Staff Only: item control page