Forward propagating reinforcement learning--biologically plausible learning method for multi-layer networks.
We introduce a biologically plausible method of implementing reinforcement learning to multi-layer neural networks. The key idea is to spatially localize the synaptic modulation induced by reinforcement signals, proceeding downstream from the initial layer to the final layer. Since reinforcement signals are known to be broadcast signals in the actual brain, we need two key assumptions, inhibitory backward connections and bypass to output units, to spatially localize the effect of delayed reinforcement without breaking the basic laws of neurophysiology.
Department of Quantum Engineering and Systems Science, Graduate School of Engineering, The University of Tokyo, 7-3-1, Hongo Bunkyo-ku, Tokyo 113, Japan. watanabe@sk.q.t.u-tokyo.ac.jp
Masataka
M
Watanabe
Tomohiro
T
Masuda
Kazuyuki
K
Aihara
D000818:Animals; D001288:Attention / Q000502:physiology; D001921:Brain / Q000502:physiology; D003198:Computer Simulation; D004298:Dopamine / Q000502:physiology; D006801:Humans; D007858:Learning / Q000502:physiology*; D016571:Neural Networks, Computer; D012054:Reinforcement, Psychology; D014793:Visual Cortex / Q000502:physiology
D004298:Dopamine