Measuring Data Quality for Dataset Selection in Offline Reinforcement Learning

• URL: http://arxiv.org/abs/2111.13461v1
• Date: Fri, 26 Nov 2021 12:22:55 GMT
• Title: Measuring Data Quality for Dataset Selection in Offline Reinforcement Learning
• Authors: Phillip Swazinna, Steffen Udluft, Thomas Runkler
• Abstract summary: Recently developed offline reinforcement learning algorithms have made it possible to learn policies directly from pre-collected datasets. Since the performance the algorithms are able to deliver depends greatly on the dataset that is presented to them, practitioners need to pick the right dataset among the available ones. We discuss ideas how to select promising datasets and propose three very simple indicators: Estimated relative return improvement (ERI), estimated action relativeity (EAS), and a combination of the two (COI)
• Score: 2.3333090554192615
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently developed offline reinforcement learning algorithms have made it possible to learn policies directly from pre-collected datasets, giving rise to a new dilemma for practitioners: Since the performance the algorithms are able to deliver depends greatly on the dataset that is presented to them, practitioners need to pick the right dataset among the available ones. This problem has so far not been discussed in the corresponding literature. We discuss ideas how to select promising datasets and propose three very simple indicators: Estimated relative return improvement (ERI) and estimated action stochasticity (EAS), as well as a combination of the two (COI), and empirically show that despite their simplicity they can be very effectively used for dataset selection.

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