- Title
- Constructing stochastic mixture policies for episodic multiobjective reinforcement learning tasks
- Creator
- Vamplew, Peter; Dazeley, Richard; Barker, Ewan; Kelarev, Andrei
- Date
- 2009
- Type
- Text; Book chapter
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/67587
- Identifier
- vital:3298
- Identifier
-
https://doi.org/10.1007/978-3-642-10439-8_35
- Identifier
- ISBN:978-3-642-10438-1
- Abstract
- Multiobjective reinforcement learning algorithms extend reinforcement learning techniques to problems with multiple conflicting objectives. This paper discusses the advantages gained from applying stochastic policies to multiobjective tasks and examines a particular form of stochastic policy known as a mixture policy. Two methods are proposed for deriving mixture policies for episodic multiobjective tasks from deterministic base policies found via scalarised reinforcement learning. It is shown that these approaches are an efficient means of identifying solutions which offer a superior match to the user’s preferences than can be achieved by methods based strictly on deterministic policies.
- Publisher
- Heidelberg, Germany Springer
- Relation
- AI 2009 : Advances in Artificial Intelligence : 22nd Australasian Joint Conference, Melbourne, Australia, December 1-4, 2009. Proceedings Chapter p. 340-349
- Rights
- Copyright Springer
- Rights
- Open Access
- Rights
- This metadata is freely available under a CCO license
- Subject
- Multiobjective; Reinforcement learning; Scalarisation; Pareto fronts
- Full Text
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