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pro vyhledávání: '"Cho, Taehyun"'
Distributional reinforcement learning improves performance by effectively capturing environmental stochasticity, but a comprehensive theoretical understanding of its effectiveness remains elusive. In this paper, we present a regret analysis for distr
Externí odkaz:
http://arxiv.org/abs/2407.21260
The field of risk-constrained reinforcement learning (RCRL) has been developed to effectively reduce the likelihood of worst-case scenarios by explicitly handling risk-measure-based constraints. However, the nonlinearity of risk measures makes it cha
Externí odkaz:
http://arxiv.org/abs/2405.18698
Publikováno v:
PMLR 216:809-818, 2023
One of the objectives of continual learning is to prevent catastrophic forgetting in learning multiple tasks sequentially, and the existing solutions have been driven by the conceptualization of the plasticity-stability dilemma. However, the converge
Externí odkaz:
http://arxiv.org/abs/2404.05555
Alleviating overestimation bias is a critical challenge for deep reinforcement learning to achieve successful performance on more complex tasks or offline datasets containing out-of-distribution data. In order to overcome overestimation bias, ensembl
Externí odkaz:
http://arxiv.org/abs/2401.03137
Distributional reinforcement learning algorithms have attempted to utilize estimated uncertainty for exploration, such as optimism in the face of uncertainty. However, using the estimated variance for optimistic exploration may cause biased data coll
Externí odkaz:
http://arxiv.org/abs/2310.16546
Autor:
Cho, Taehyun
Although many scholars attempted to define and categorize alternative narratives, a new trend in narrative that has proliferated at the turn of the 21st century, there is no consensus. To understand recent alternative narrative films more comprehensi
Externí odkaz:
http://hdl.handle.net/2152/26551
Publikováno v:
In Powder Technology 2011 208(1):7-19
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