2 Citaten (Scopus)
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Samenvatting

In multi-objective optimization, learning all the policies that reach Pareto-efficient solutions is an expensive process. The set of optimal policies can grow exponentially with the number of objectives, and recovering all solutions requires an exhaustive exploration of the entire state space. We propose Pareto Conditioned Networks (PCN), a method that uses a single neural network to encompass all non-dominated policies. PCN associates every past transition with its episode's return. It trains the network such that, when conditioned on this same return, it should reenact said transition. In doing so we transform the optimization problem into a classification problem. We recover a concrete policy by conditioning the network on the desired Pareto-efficient solution. Our method is stable as it learns in a supervised fashion, thus avoiding moving target issues. Moreover, by using a single network, PCN scales efficiently with the number of objectives. Finally, it makes minimal assumptions on the shape of the Pareto front, which makes it suitable to a wider range of problems than previous state-of-the-art multi-objective reinforcement learning algorithms.
Originele taal-2English
TitelThe 21st International Conference on Autonomous Agents and Multiagent Systems
UitgeverijIFAAMAS
Pagina's1110-1118
Aantal pagina's9
ISBN van elektronische versie9781713854333
StatusPublished - 9 mei 2022
Evenement21st International Conference on Autonomous Agents and Multi-agent System -
Duur: 9 mei 202213 mei 2022
Congresnummer: 21
https://aamas2022-conference.auckland.ac.nz

Conference

Conference21st International Conference on Autonomous Agents and Multi-agent System
Verkorte titelAAMAS
Periode9/05/2213/05/22
Internet adres

Bibliografische nota

Funding Information:
The authors would like to acknowledge FWO (Fonds Wetenschappelijk Onderzoek) for their support through the SB grant of Eugenio Bargiacchi (#1SA2820N). This research was additionally supported by funding from the Flemish Government under the “Onderzoeksprogramma Artificiële Intelligentie (AI) Vlaanderen” programme. We would also like to thank Diederik M. Roijers for helpful feedback.

Funding Information:
by funding from the Flemish Government under the “Onderzoek-sprogramma Artificiële Intelligentie (AI) Vlaanderen” programme.

Funding Information:
The authors would like to acknowledge FWO (Fonds Wetenschap-pelijk Onderzoek) for their support through the SB grant of Eugenio Bargiacchi (#1SA2820N). This research was additionally supported

Publisher Copyright:
© 2022 International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All rights reserved

Copyright:
Copyright 2022 Elsevier B.V., All rights reserved.

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