Embedding multi-agent reinforcement learning into behavior trees with unexpected interruptions
Embedding multi-agent reinforcement learning into behavior trees with unexpected interruptions
About this item
Full title
Author / Creator
Publisher
Cham: Springer International Publishing
Journal title
Language
English
Formats
Publication information
Publisher
Cham: Springer International Publishing
Subjects
More information
Scope and Contents
Contents
Behavior trees have attracted great interest in computer games and robotic applications. However, it lacks the learning ability for dynamic environments. Previous works combining behavior trees with reinforcement learning either need to construct an independent sub-scenario or train the learning method over the whole game, which is not suited for c...
Alternative Titles
Full title
Embedding multi-agent reinforcement learning into behavior trees with unexpected interruptions
Authors, Artists and Contributors
Author / Creator
Identifiers
Primary Identifiers
Record Identifier
TN_cdi_doaj_primary_oai_doaj_org_article_9b0cfb1787954f1287281e1a23a354d8
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_9b0cfb1787954f1287281e1a23a354d8
Other Identifiers
ISSN
2199-4536
E-ISSN
2198-6053
DOI
10.1007/s40747-023-01326-7
