Exclude
发表于 2025-3-23 12:17:55
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束缚
发表于 2025-3-23 16:24:38
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龙卷风
发表于 2025-3-23 21:12:20
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cathartic
发表于 2025-3-23 22:37:15
Tuning the Discount Factor in Order to Reach Average Optimality on Deterministic MDPsce of being insensitive to different policies with equivalent average. This paper analyzes the impact of such different criteria on a series of experiments, and then provides a threshold for the discount factor in order to ensure average optimality for discounted-optimal policies in the deterministic case.
爆米花
发表于 2025-3-24 04:47:39
Conference proceedings 2018uling; Machine Learning; Industrial Applications of Artificial Intelligence; Planning and Scheduling in Action; Machine Learning in Action; Applications of Machine Learning; and Applications of Agent Systems and Genetic Algorithms..
兽群
发表于 2025-3-24 07:31:51
0302-9743and Scheduling; Machine Learning; Industrial Applications of Artificial Intelligence; Planning and Scheduling in Action; Machine Learning in Action; Applications of Machine Learning; and Applications of Agent Systems and Genetic Algorithms..978-3-030-04190-8978-3-030-04191-5Series ISSN 0302-9743 Series E-ISSN 1611-3349
碳水化合物
发表于 2025-3-24 14:43:22
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随意
发表于 2025-3-24 15:26:27
https://doi.org/10.1057/9781137521903 and rule based systems can take advantage of this parallelism. It is hoped that this work will support further exploration of parallel neural and rule based systems, and support further work in cognitive modelling and cognitive architecture.
SLUMP
发表于 2025-3-24 19:38:28
Helen Silva de Joyce,Susan Feez properties of the pairs drive exploitation. We adopt curriculum learning to organise the ordering of pairs at training time using similarity knowledge as a heuristic for pair sorting. The results of our experimental evaluation show that these strategies are key to optimising training.
Cerumen
发表于 2025-3-25 02:17:51
Research and Practice in Applied Linguisticsance of an existing Genetic Algorithm that does Work Area Optimization. We will also compare our approach with prior knowledge generated with the help of human experts with years of experience in the field. We show that our new approach is as good as or better in generating the prior knowledge when compared to human experts.