Aug 1, 2022 · We classify various SPI approaches from the literature into two groups, based on how they utilize the uncertainty of state-action pairs.
Jan 20, 2023 · We classify various SPI approaches from the literature into two groups, based on how they utilize the uncertainty of state-action pairs.
Abstract. Safe Policy Improvement (SPI) is an important technique for offline reinforcement learning in safety critical applications as it improves the behavior ...
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A heuristic adaptation, Lower-Approx-Soft-SPIBB, yields the best performance among all SPIBB algorithms in extensive experiments on two benchmarks, ...
Aug 1, 2022 · Safe Policy Improvement (SPI) aims at provable guarantees that a learned policy is at least approximately as good as a given baseline policy.
Safe Policy Improvement Approachesand their limitations. In: Agents and Artificial Intelligence. Series: Lecture Notes in Artificial Intelligence. Springer ...
Mar 25, 2024 · ... Safe Policy Improvement Approaches and Their Limitations. 14th International Conference on Agents and Artificial Intelligence, ICAART 2022 ...
Jan 28, 2022 · Safe Policy Improvement (SPI) aims at provable guarantees that a learned policy is at least approximately as good as a given baseline policy.
Missing: Limitations. | Show results with:Limitations.
A taxonomy of SPI algorithms is introduced and empirically show an interesting property of two classes of SPI algorithm: while the mean performance of ...
We presented a new approach to safe policy improvement that reduces the ... donian offline policy improvement with safety constraints in finite MDPs ...
Missing: Limitations. | Show results with:Limitations.