| Budget Amount *help |
¥4,680,000 (Direct Cost: ¥3,600,000、Indirect Cost: ¥1,080,000)
Fiscal Year 2019: ¥650,000 (Direct Cost: ¥500,000、Indirect Cost: ¥150,000)
Fiscal Year 2018: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2017: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2016: ¥780,000 (Direct Cost: ¥600,000、Indirect Cost: ¥180,000)
Fiscal Year 2015: ¥650,000 (Direct Cost: ¥500,000、Indirect Cost: ¥150,000)
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| Outline of Final Research Achievements |
I could not reach the initial goal that is to establish the algorithm of reinforcement learning using a chaos neural network (NN), which I have proposed, and then the emergence of “primitive thinking” on the basis of the hypothesis that “exploration” grows into “thinking” through reinforcement learning.
On the other hand, I have proposed an index “sensitivity” in each neuron to control the chaoticity of the network globally, and also “sensitivity adjustment learning” to learn it. It can be used as an index for generating chaos, and it can also be used to solve the vanishing/exploding gradient problem in gradient-based learning. Furthermore, completely new reinforcement learning named “Dynamic Reinforcement Learning” in which the present output value is not learned directly but dynamics is learned by adjusting the sensitivity according to TD error (the difference of actual state value from its prediction), has come up.
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