7 citations · 9 across the 5 of their papers we have counts for
5 papers
Uncovering Latent Phase Structures and Branching Logic in Locomotion Policies: A Case Study on HalfCheetah
Daisuke Yasui, Toshitaka Matsuki, Hiroshi Sato
In locomotion control tasks, Deep Reinforcement Learning (DRL) has demonstrated high performance; however, the decision-making process of the learned policy remains a black box, ma…
Chaos-based reinforcement learning with TD3
Toshitaka Matsuki, Yusuke Sakemi, Kazuyuki Aihara
Chaos-based reinforcement learning (CBRL) is a method in which the agent's internal chaotic dynamics drives exploration. However, the learning algorithms in CBRL have not been thor…
Learning Reservoir Dynamics with Temporal Self-Modulation
Yusuke Sakemi, Sou Nobukawa, Toshitaka Matsuki +2
Reservoir computing (RC) can efficiently process time-series data by transferring the input signal to randomly connected recurrent neural networks (RNNs), which are referred to as…
Deep Q-network using reservoir computing with multi-layered readout
Toshitaka Matsuki
Recurrent neural network (RNN) based reinforcement learning (RL) is used for learning context-dependent tasks and has also attracted attention as a method with remarkable learning…
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…