Estimating internal variables and parameters of a learning agent by a particle filter

被引:0
|
作者
Samejima, K [1 ]
Doya, K [1 ]
Ueda, Y [1 ]
Kimura, M [1 ]
机构
[1] JST, CRST, ATR Computat Neurosci Labs, Dept Computat Neurobiol, Kyoto 6190288, Japan
来源
ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 16 | 2004年 / 16卷
关键词
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
When we model a higher order functions, such as learning and memory, we face a difficulty of comparing neural activities with hidden variables that depend on the history of sensory and motor signals and the dynamics of the network. Here, we propose novel method for estimating hidden variables of a learning agent, such as connection weights from sequences of observable variables. Bayesian estimation is a method to estimate the posterior probability of hidden variables from observable data sequence using a dynamic model of hidden and observable variables. In this paper, we apply particle filter for estimating internal parameters and metaparameters of a reinforcement learning model. We verified the effectiveness of the method using both artificial data and real animal behavioral data.
引用
收藏
页码:1335 / 1342
页数:8
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