Our analysis shows that algorithms using a combination of gap-increasing and max operators are resilient to stochastic errors, but not to non-stochastic errors.

Algorithms using a combination of gap-increasing and softmax operators are much more effective and may asymptotically outperform algorithms with the max ...

Conservative value iteration is proposed and analyzed, which unifies value iteration, soft value iterations, advantage learning, and dynamic policy ...

Theoretical analysis of efficiency and robustness of softmax and gap-increasing operators in reinforcement learning T Kozuno, E Uchibe, K Doya

Mar 20, 2022 · Theoreti- cal analysis of efficiency and robustness of softmax and gap-increasing operators in reinforcement learning. In The. 22nd ...

Oct 4, 2024 · Theoretical Analysis of Efficiency and Robustness of Softmax and Gap-Increasing Operators in Reinforcement Learning. AISTATS 2019: 2995-3003.

Theoretical analysis of efficiency and robustness of softmax and gap-increasing operators in RL. In AISTATS, 2019. [13] Baird III, L. C. Reinforcement Learning ...

Theoretical Analysis of Efficiency and Robustness of Softmax and Gap-Increasing Operators in Reinforcement Learning · hmtl icon · Tadashi Kozuno, Eiji Uchibe ...

Kozuno, T.; Uchibe, E.; and Doya, K. 2019. Theoretical analysis of efficiency and robustness of softmax and gap-increasing operators in reinforcement learning.

May 16, 2022 · Theoreti- cal analysis of efficiency and robustness of softmax and gap-increasing operators in reinforcement learning. In. Proceedings of the ...