15 citations · 26 across the 4 of their papers we have counts for
4 papers
Stability-Certified Reinforcement Learning via Spectral Normalization
Ryoichi Takase, Nobuyuki Yoshikawa, Toshisada Mariyama +1
In this article, two types of methods from different perspectives based on spectral normalization are described for ensuring the stability of the system controlled by a neural netw…
Deep Reactive Planning in Dynamic Environments
Kei Ota, Devesh K. Jha, Tadashi Onishi +5
The main novelty of the proposed approach is that it allows a robot to learn an end-to-end policy which can adapt to changes in the environment during execution. While goal conditi…
Can Increasing Input Dimensionality Improve Deep Reinforcement Learning?
Kei Ota, Tomoaki Oiki, Devesh K. Jha +2
Deep reinforcement learning (RL) algorithms have recently achieved remarkable successes in various sequential decision making tasks, leveraging advances in methods for training lar…
Trajectory Optimization for Unknown Constrained Systems using Reinforcement Learning
Kei Ota, Devesh K. Jha, Tomoaki Oiki +4
In this paper, we propose a reinforcement learning-based algorithm for trajectory optimization for constrained dynamical systems. This problem is motivated by the fact that for mos…