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Generalized Policy Improvement Algorithms with Theoretically Supported Sample Reuse
- Publication Year :
- 2022
-
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.<br />Comment: Accepted for publication in IEEE Transactions on Automatic Control
Details
- Database :
- arXiv
- Publication Type :
- Report
- Accession number :
- edsarx.2206.13714
- Document Type :
- Working Paper
- Full Text :
- https://doi.org/10.1109/TAC.2024.3454011