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arXiv · 2206.13714

Generalized Policy Improvement Algorithms with Theoretically Supported Sample Reuse

James Queeney·Ioannis Ch. Paschalidis·Christos G. Cassandras
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Abstract

We develop a new class of model-free deep reinforcement learning algorithms for data-driven, learning-based control. Our Generalized Policy Improvement algorithms combine the policy improvement guarantees of on-policy methods with the efficiency of sample reuse, addressing a trade-off between two important deployment requirements for real-world control: (i) practical performance guarantees and (ii) data efficiency. We demonstrate the benefits of this new class of algorithms through extensive experimental analysis on a broad range of simulated control tasks.

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BibTeXRIS

James Queeney, Ioannis Ch. Paschalidis, Christos G. Cassandras. 2022-06-28. Generalized Policy Improvement Algorithms with Theoretically Supported Sample Reuse. https://doi.org/10.1109/tac.2024.3454011

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