Hierarchical state-abstracted and socially augmented Q-Learning for reducing complexity in agent-based learning-中国期刊网

摘要 Aprimarychallengeofagent-basedpolicylearningincomplexanduncertainenvironmentsisescalatingcomputationalcomplexitywiththesizeofthetaskspace(actionchoicesandworldstates)andthenumberofagents.Nonetheless,thereisampleevidenceinthenaturalworldthathigh-functioningsocialmammalslearntosolvecomplexproblemswithease,bothindividuallyandcooperatively.Thisabilitytosolvecomputationallyintractableproblemsstemsfrombothbraincircuitsforhierarchicalrepresentation...

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Hierarchical state-abstracted and socially augmented Q-Learning for reducing complexity in agent-based learning

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Hierarchical state-abstracted and socially augmented Q-Learning for reducing complexity in agent-based learning

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