Local Policy Search in a Convex Space and Conservative Policy Iteration as Boosted Policy Search
Identifieur interne : 000936 ( Main/Exploration ); précédent : 000935; suivant : 000937Local Policy Search in a Convex Space and Conservative Policy Iteration as Boosted Policy Search
Auteurs : Bruno Scherrer [France] ; Matthieu Geist [France]Source :
Abstract
Local Policy Search is a popular reinforcement learning approach for handling large state spaces. Formally, it searches locally in a parameterized policy space in order to maximize the associated value function averaged over some pre-defined distribution. The best one can hope in general from such an approach is to get a local optimum of this criterion. The first contribution of this article is the following surprising result: if the policy space is convex, any (approximate) local optimum enjoys a global performance guarantee. Unfortunately, the convexity assumption is strong: it is not satisfied by commonly used parameterizations and designing a parameterization that induces this property seems hard. A natural so-lution to alleviate this issue consists in deriving an algorithm that solves the local policy search problem using a boosting approach (constrained to the convex hull of the policy space). The resulting algorithm turns out to be a slight generalization of conservative policy iteration; thus, our second contribution is to highlight an original connection between local policy search and approximate dynamic pro-gramming.
Url:
DOI: 10.1007/978-3-662-44845-8_3
Affiliations:
- France
- Franche-Comté, Grand Est, Lorraine (région)
- Besançon, Metz, Nancy
- Université Paul Verlaine - Metz, Université de Bourgogne Franche-Comté, Université de Franche-Comté, Université de Lorraine
Links toward previous steps (curation, corpus...)
- to stream Hal, to step Corpus: 002F45
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- to stream Main, to step Merge: 000937
- to stream Main, to step Curation: 000936
Le document en format XML
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<front><div type="abstract" xml:lang="en">Local Policy Search is a popular reinforcement learning approach for handling large state spaces. Formally, it searches locally in a parameterized policy space in order to maximize the associated value function averaged over some pre-defined distribution. The best one can hope in general from such an approach is to get a local optimum of this criterion. The first contribution of this article is the following surprising result: if the policy space is convex, any (approximate) local optimum enjoys a global performance guarantee. Unfortunately, the convexity assumption is strong: it is not satisfied by commonly used parameterizations and designing a parameterization that induces this property seems hard. A natural so-lution to alleviate this issue consists in deriving an algorithm that solves the local policy search problem using a boosting approach (constrained to the convex hull of the policy space). The resulting algorithm turns out to be a slight generalization of conservative policy iteration; thus, our second contribution is to highlight an original connection between local policy search and approximate dynamic pro-gramming.</div>
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<region><li>Franche-Comté</li>
<li>Grand Est</li>
<li>Lorraine (région)</li>
</region>
<settlement><li>Besançon</li>
<li>Metz</li>
<li>Nancy</li>
</settlement>
<orgName><li>Université Paul Verlaine - Metz</li>
<li>Université de Bourgogne Franche-Comté</li>
<li>Université de Franche-Comté</li>
<li>Université de Lorraine</li>
</orgName>
</list>
<tree><country name="France"><region name="Grand Est"><name sortKey="Scherrer, Bruno" sort="Scherrer, Bruno" uniqKey="Scherrer B" first="Bruno" last="Scherrer">Bruno Scherrer</name>
</region>
<name sortKey="Geist, Matthieu" sort="Geist, Matthieu" uniqKey="Geist M" first="Matthieu" last="Geist">Matthieu Geist</name>
</country>
</tree>
</affiliations>
</record>
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