Probabilistic inference for determining options in reinforcement learning
Probabilistic inference for determining options in reinforcement learning
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Publisher
New York: Springer US
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Language
English
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Publisher
New York: Springer US
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Scope and Contents
Contents
Tasks that require many sequential decisions or complex solutions are hard to solve using conventional reinforcement learning algorithms. Based on the semi Markov decision process setting (SMDP) and the option framework, we propose a model which aims to alleviate these concerns. Instead of learning a single monolithic policy, the agent learns a set...
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Full title
Probabilistic inference for determining options in reinforcement learning
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Record Identifier
TN_cdi_proquest_miscellaneous_1835621254
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_miscellaneous_1835621254
Other Identifiers
ISSN
0885-6125
E-ISSN
1573-0565
DOI
10.1007/s10994-016-5580-x
