PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Approximate Dynamic Programming with Affine ADDs
Scott Sanner, W Uther and KV Delgado
In: Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems (AAMAS-10), Toronto, Canada(2010).

Abstract

The Affine ADD (AADD) is an extension of the Algebraic Decision Diagram (ADD) that compactly represents context specific, additive and multiplicative structure in functions from a discrete domain to a real-valued range. In this paper, we introduce a novel algorithm for efficiently finding AADD approximations that we use to develop the MADCAP algorithm for AADD-based structured approximate dynamic programming (ADP) with factored MDPs. MADCAP requires less time and space to achieve comparable or better approximate solutions than the current state-of-the-art ADD-based ADP algorithm of APRICODD and can provide approximate solutions for problems with context-specific, additive and multiplicative structure on which APRICODD runs out of memory.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:7456
Deposited By:Wray Buntine
Deposited On:17 March 2011