Monografie

Efficient Preference-based Reinforcement Learning

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Language: Englisch

Identifier: 1147968411

Subject

Hochschulschrift

Contributor

Wirth, Christian
Fürnkranz, Johannes
Neumann, Gerhard

URN: http://nbn-resolving.org/urn:nbn:de:tuda-tuprints-69522

Rights: Der Zugriff auf das Objekt ist unbeschränkt möglich.

Last update: 26.01.2023, 1:55 PM CET

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Deutsche Nationalbibliothek

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Monografie

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Wirth, Christian
Fürnkranz, Johannes
Neumann, Gerhard

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Efficient Preference-based Reinforcement Learning

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Other Objects (12)

Latent Space Reinforcement Learning

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Using Mean Embeddings for State Estimation and Reinforcement Learning

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Robot Skill Representation, Learning and Control with Probabilistic Movement Primitives

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Preference-based Representation Learning for Collections

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Expected Information Maximization: Using the I-Projection for Mixture Density Estimation

Catching heuristics are optimal control policies

Projections for Approximate Policy Iteration Algorithms

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Sample-Efficient I-Projections for Robot Learning

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Robot Skill Representation, Learning and Control with Probabilistic Movement Primitives

Learning Coupled Forward-Inverse Models with Combined Prediction Errors

Preference-based Representation Learning for Collections

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Demonstration based trajectory optimization for generalizable robot motions

Expected Information Maximization: Using the I-Projection for Mixture Density Estimation

Catching heuristics are optimal control policies

Projections for Approximate Policy Iteration Algorithms

Latent Space Reinforcement Learning

Sample-Efficient I-Projections for Robot Learning

Using Mean Embeddings for State Estimation and Reinforcement Learning

Efficient Gradient-Free Variational Inference using Policy Search

Robot Skill Representation, Learning and Control with Probabilistic Movement Primitives

Learning Coupled Forward-Inverse Models with Combined Prediction Errors

Preference-based Representation Learning for Collections

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Expected Information Maximization: Using the I-Projection for Mixture Density Estimation

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Projections for Approximate Policy Iteration Algorithms

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