.png){kind=icon}
返回
Discovering Implied Serial Order Through Model-Free and Model-Based Learning
.png){kind=icon}
OA
AI
DOI：10.3389/fnins.2019.00878.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Humans and animals can learn to order a list of items without relying on explicit spatial or temporal cues. To do so, they appear to make use of transitivity, a property of all ordered sets. Here, we summarize relevant research on the transitive inference (TI) paradigm and its relationship to learning the underlying order of an arbitrary set of items. We compare six computational models of TI performance, three of which are modelfree (Q-learning, Value Transfer, and REMERGE) and three of which are model-based (RL-Elo, Sequential Monte Carlo, and Betasort). Our goal is to assess the ability of these models to produce empirically observed features of TI behavior. Model-based approaches perform better under a wider range of scenarios, but no single model explains the full scope of behaviors reported in the TI literature.
Keyword:
reinforcement learning
model-free learning
model-based learning
cognitive maps
transitive inference
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.2
论文数:
1.6W
被引数:
5.3W
机构
引用论文
.png){kind=icon}
Effect of Pencycuron on the Osmotic Stability of Protoplasts of <i>Rhizoctonia solani</i>Pencycuron对Rhizoctonia solani原生质体渗透稳定性的影响
OAAI
OAAI分享收藏Study of bi-directional buck-boost converter topologies for application in electrical vehicle motor drives应用于电动汽车电机驱动的双向buck-boost变换器拓扑研究
PREAI
分享收藏
查看更多内容