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Learning to reinforcement learn

, , , , , , , , и . (2016)cite arxiv:1611.05763Comment: 17 pages, 7 figures, 1 table.

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Learning to reinforcement learn, , , , , , , , и . (2016)cite arxiv:1611.05763Comment: 17 pages, 7 figures, 1 table.Object-based attention for spatio-temporal reasoning: Outperforming neuro-symbolic models with flexible distributed architectures., , , и . CoRR, (2020)Collaborating with Humans without Human Data., , , , и . NeurIPS, стр. 14502-14515. (2021)Attention over Learned Object Embeddings Enables Complex Visual Reasoning., , , , и . NeurIPS, стр. 9112-9124. (2021)Cognitive Model Discovery via Disentangled RNNs., , , и . NeurIPS, (2023)Alchemy: A benchmark and analysis toolkit for meta-reinforcement learning agents., , , , , , , , , и 7 other автор(ы). NeurIPS Datasets and Benchmarks, (2021)Fine-tuning language models to find agreement among humans with diverse preferences., , , , , , , , , и 1 other автор(ы). NeurIPS, (2022)SIMONe: View-Invariant, Temporally-Abstracted Object Representations via Unsupervised Video Decomposition., , , , , , , и . NeurIPS, стр. 20146-20159. (2021)Minimum Description Length Control., , , и . ICLR, OpenReview.net, (2023)Cognitive Psychology for Deep Neural Networks: A Shape Bias Case Study., , , и . ICML, том 70 из Proceedings of Machine Learning Research, стр. 2940-2949. PMLR, (2017)