Non-Parametric Policy Gradients: A Unified Treatment of Propositional and Relational Domains
published: Aug. 4, 2008, recorded: July 2008, views: 3064
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Policy gradient approaches are a powerful instrument for learning how to interact with the environment.Existing approaches have focused on propositional and continuous domains only. Without extensive feature engineering, it is difficult -- if not impossible -- to apply them within structured domains, in which e.g. there is a varying number of objects and relations among them. In this paper, we describe a non-parametric policy gradient approach -- called NPPG -- that overcomes this limitation. The key idea is to apply Friedmann's gradient boosting: policies are represented as a weighted sum of regression models grown in an stage-wise optimization. Employing off-the-shelf regression learners, NPPG can deal with propositional, continuous, and relational domains in a unified way. Our experimental results show that it can even improve on established results.
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