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Provably Robust Blackbox Optimization for Reinforcement Learning.

, , , , , , , , и . CoRL, том 100 из Proceedings of Machine Learning Research, стр. 683-696. PMLR, (2019)

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Provably Robust Blackbox Optimization for Reinforcement Learning., , , , , , , , и . CoRL, том 100 из Proceedings of Machine Learning Research, стр. 683-696. PMLR, (2019)Practical Nonisotropic Monte Carlo Sampling in High Dimensions via Determinantal Point Processes., , , и . AISTATS, том 108 из Proceedings of Machine Learning Research, стр. 1363-1374. PMLR, (2020)On Optimism in Model-Based Reinforcement Learning., , , , и . CoRR, (2020)Human-Timescale Adaptation in an Open-Ended Task Space., , , , , , , , , и 18 other автор(ы). CoRR, (2023)Towards tractable optimism in model-based reinforcement learning., , , , и . UAI, том 161 из Proceedings of Machine Learning Research, стр. 1413-1423. AUAI Press, (2021)Grounding Aleatoric Uncertainty for Unsupervised Environment Design., , , , , , , и . NeurIPS, (2022)Insights From the NeurIPS 2021 NetHack Challenge., , , , , , , , , и 19 other автор(ы). NeurIPS (Competition and Demos), том 176 из Proceedings of Machine Learning Research, стр. 41-52. PMLR, (2021)MiniHack the Planet: A Sandbox for Open-Ended Reinforcement Learning Research., , , , , , , , , и . NeurIPS Datasets and Benchmarks, (2021)From Complexity to Simplicity: Adaptive ES-Active Subspaces for Blackbox Optimization., , , , и . NeurIPS, стр. 10299-10309. (2019)Replay-Guided Adversarial Environment Design., , , , , и . NeurIPS, стр. 1884-1897. (2021)