Deterministic limit of temporal difference reinforcement learning for stochastic games
Deterministic limit of temporal difference reinforcement learning for stochastic games
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Ithaca: Cornell University Library, arXiv.org
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English
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Ithaca: Cornell University Library, arXiv.org
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Reinforcement learning in multiagent systems has been studied in the fields of economic game theory, artificial intelligence and statistical physics by developing an analytical understanding of the learning dynamics (often in relation to the replicator dynamics of evolutionary game theory). However, the majority of these analytical studies focuses...
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Deterministic limit of temporal difference reinforcement learning for stochastic games
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TN_cdi_proquest_journals_2110113154
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https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2110113154
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E-ISSN
2331-8422
DOI
10.48550/arxiv.1809.07225
