14 citations · 30 across the 5 of their papers we have counts for
5 papers
Dynamic Reinforcement Learning for Actors
Katsunari Shibata
Dynamic Reinforcement Learning (Dynamic RL), proposed in this paper, directly controls system dynamics, instead of the actor (action-generating neural network) outputs at each mome…
Sensitivity - Local Index to Control Chaoticity or Gradient Globally -
Katsunari Shibata, Takuya Ejima, Yuki Tokumaru +1
Here, we introduce a fully local index named "sensitivity" for each neuron to control chaoticity or gradient globally in a neural network (NN). We also propose a learning method to…
New Reinforcement Learning Using a Chaotic Neural Network for Emergence of "Thinking" - "Exploration" Grows into "Thinking" through Learning -
Katsunari Shibata, Yuki Goto
Expectation for the emergence of higher functions is getting larger in the framework of end-to-end reinforcement learning using a recurrent neural network. However, the emergence o…
Communications that Emerge through Reinforcement Learning Using a (Recurrent) Neural Network
Katsunari Shibata
Communication is not only an action of choosing a signal, but needs to consider the context and sensor signals. It also needs to decide what information is communicated and how it…
Functions that Emerge through End-to-End Reinforcement Learning - The Direction for Artificial General Intelligence -
Katsunari Shibata
Recently, triggered by the impressive results in TV-games or game of Go by Google DeepMind, end-to-end reinforcement learning (RL) is collecting attentions. Although little is know…