Linked Life Data

15309543

Source:http://linkedlifedata.com/resource/pubmed/id/15309543

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
1
pubmed:dateCreated
2004-8-23
pubmed:abstractText
The present paper discusses an optimal learning control method using reinforcement learning for biological systems with a redundant actuator. It is difficult to apply reinforcement learning to biological control systems because of the redundancy in muscle activation space. We solve this problem with the following method. First, we divide the control input space into two subspaces according to a priority order of learning and restrict the search noise for reinforcement learning to the first priority subspace. Then the constraint is reduced as the learning progresses, with the search space extending to the second priority subspace. The higher priority subspace is designed so that the impedance of the arm can be high. A smooth reaching motion is obtained through reinforcement learning without any previous knowledge of the arm's dynamics.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Jul
pubmed:issn
0340-1200
pubmed:author
pubmed:issnType
Print
pubmed:volume
91
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
10-22
pubmed:meshHeading
pubmed:year
2004
pubmed:articleTitle
Biological arm motion through reinforcement learning.
pubmed:affiliation
Sensory and Motor Research Group, Human and Information Science Laboratory, NTT Communication Science Laboratories 3-1, Morinosato-Wakamiya, 243-01, Atsugi-shi, Japan. izawa@idea.brl.ntt.co.jp
pubmed:publicationType
Journal Article