A Brain-like Learning System with Supervised, Unsupervised and Reinforcement Learning

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  • Sasakawa Takafumi
    Graduate School of Information, Production and Systems, Waseda University
  • Hu Jinglu
    Graduate School of Information, Production and Systems, Waseda University
  • Hirasawa Kotaro
    Graduate School of Information, Production and Systems, Waseda University

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  • 教師あり学習·教師なし学習·強化学習を複合したbrain-like学習システム
  • 教師あり学習・教師なし学習・強化学習を複合したbrain-like学習システム
  • キョウシ アリ ガクシュウ キョウシ ナシ ガクシュウ キョウカ ガクシュウ オ フクゴウ シタ brain like ガクシュウ システム

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Our brain has three different learning paradigms: supervised, unsupervised and reinforcement learning. And it is suggested that those learning paradigms relate deeply to the cerebellum, cerebral cortex and basal ganglia in the brain, respectively. Inspired by these knowledge of brain, we present a brain-like learning system with those three different learning algorithms. The proposed system consists of three parts: the supervised learning (SL) part, the unsupervised learning (UL) part and the reinforcement learning (RL) part. The SL part, corresponding to the cerebellum of brain, learns an input-output mapping by supervised learning. The UL part, corresponding to the cerebral cortex of brain, is a competitive learning network, and divides an input space to subspaces by unsupervised learning. The RL part, corresponding to the basal ganglia of brain, optimizes the model performance by reinforcement learning. Numerical simulations show that the proposed brain-like learning system optimizes its performance automatically and has superior performance to an ordinary neural network.

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