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Derivative-Free Methods for Policy Optimization: Guarantees for Linear Quadratic Systems.

, , , , , и . AISTATS, том 89 из Proceedings of Machine Learning Research, стр. 2916-2925. PMLR, (2019)

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Derivative-Free Methods for Policy Optimization: Guarantees for Linear Quadratic Systems., , , , , и . AISTATS, том 89 из Proceedings of Machine Learning Research, стр. 2916-2925. PMLR, (2019)An Efficient, Generalized Bellman Update For Cooperative Inverse Reinforcement Learning., , , , , и . ICML, том 80 из Proceedings of Machine Learning Research, стр. 3391-3399. PMLR, (2018)Pragmatic-Pedagogic Value Alignment., , , , , , , , , и . ISRR, том 10 из Springer Proceedings in Advanced Robotics, стр. 49-57. Springer, (2017)Complete Policy Regret Bounds for Tallying Bandits., , и . COLT, том 178 из Proceedings of Machine Learning Research, стр. 5146-5174. PMLR, (2022)PTPerf: On the Performance Evaluation of Tor Pluggable Transports., , , и . IMC, стр. 501-525. ACM, (2023)Weighted Tallying Bandits: Overcoming Intractability via Repeated Exposure Optimality., , , и . ICML, том 202 из Proceedings of Machine Learning Research, стр. 23590-23609. PMLR, (2023)Sample Efficient Reinforcement Learning In Continuous State Spaces: A Perspective Beyond Linearity., , , и . ICML, том 139 из Proceedings of Machine Learning Research, стр. 7412-7422. PMLR, (2021)Breaking an image encryption scheme based on chaotic synchronization phenomenon., , и . IC3, стр. 1-4. IEEE Computer Society, (2016)When Is Generalizable Reinforcement Learning Tractable?, , и . NeurIPS, стр. 8032-8045. (2021)