Reward-Based Learning of a Memory-Required Task Based on the Internal Dynamics of a Chaotic Neural Network. - Toshitaka Matsuki; Katsunari Shibata (ICONIP (1), 2016)

Reinforcement Learning of a Memory Task Using an Echo State Network with Multi-layer Readout. - Toshitaka Matsuki; Katsunari Shibata (RiTA, 2017)

Q-learning with exploration driven by internal dynamics in chaotic neural network. - Toshitaka Matsuki; Souya Inoue; Katsunari Shibata (IJCNN, 2020)

Adaptive balancing of exploration and exploitation around the edge of chaos in internal-chaos-based learning. - Toshitaka Matsuki; Katsunari Shibata (Neural Networks, 2020)

Sensitivity - Local index to control chaoticity or gradient globally -. - Katsunari Shibata; Takuya Ejima; Yuki Tokumaru; Toshitaka Matsuki (Neural Networks, 2021)

Deep Q-network using reservoir computing with multi-layered readout. - Toshitaka Matsuki (CoRR, 2022)

Data-driven Response Estimation-based Tuning and its Validation Using a Ball-and-Beam System. - Takao Sato; Yuta Sakai; Natsuki Kawaguchi; Masayoshi Hara; Toshitaka Matsuki; Masanori Takahashi; Orlando Arrieta; Ramón Vilanova (ETFA, 2023)

Learning Reservoir Dynamics with Temporal Self-Modulation. - Yusuke Sakemi; Sou Nobukawa; Toshitaka Matsuki; Takashi Morie; Kazuyuki Aihara (CoRR, 2023)

Chaos-based reinforcement learning with TD3. - Toshitaka Matsuki; Yusuke Sakemi; Kazuyuki Aihara (CoRR, 2024)