Safe multi-agent motion planning under uncertainty for drones using filtered reinforcement learning

no code implementations • 31 Oct 2023 • Sleiman Safaoui, Abraham P. Vinod, Ankush Chakrabarty, Rien Quirynen, Nobuyuki Yoshikawa, Stefano Di Cairano

For this problem, we present a tractable motion planner that builds upon the strengths of reinforcement learning and constrained-control-based trajectory planning.

Collision Avoidance Motion Planning +2

SupeRBNN: Randomized Binary Neural Network Using Adiabatic Superconductor Josephson Devices

no code implementations • 21 Sep 2023 • Zhengang Li, Geng Yuan, Tomoharu Yamauchi, Zabihi Masoud, Yanyue Xie, Peiyan Dong, Xulong Tang, Nobuyuki Yoshikawa, Devesh Tiwari, Yanzhi Wang, Olivia Chen

Specifically, we investigate the randomized behavior of the AQFP devices and analyze the impact of crossbar size on current attenuation, subsequently formulating the current amplitude into the values suitable for use in BNN computation.

Stability-Certified Reinforcement Learning via Spectral Normalization

no code implementations • 26 Dec 2020 • Ryoichi Takase, Nobuyuki Yoshikawa, Toshisada Mariyama, Takeshi Tsuchiya

While explicitly including the stability condition, the first method may provide an insufficient performance on the neural network controller due to its strict stability condition.

reinforcement-learning Reinforcement Learning (RL)

A Stochastic-Computing based Deep Learning Framework using Adiabatic Quantum-Flux-Parametron SuperconductingTechnology

no code implementations • 22 Jul 2019 • Ruizhe Cai, Ao Ren, Olivia Chen, Ning Liu, Caiwen Ding, Xuehai Qian, Jie Han, Wenhui Luo, Nobuyuki Yoshikawa, Yanzhi Wang

Further, the application of SC has been investigated in DNNs in prior work, and the suitability has been illustrated as SC is more compatible with approximate computations.