13 citations · 16 across the 9 of their papers we have counts for
5 papers · 1 filter
Application of Deep Reinforcement Learning to Event-Triggered Control for Networked Artificial Pancreas Systems
Junya Ikemoto, Satoshi Maruyama, Kazumune Hashimoto
This paper proposes a deep reinforcement learning (DRL)-based event-triggered controller design for networked artificial pancreas (AP) systems. Although existing DRL-based AP contr…
Data-Driven Synthesis of Probabilistic Controlled Invariant Sets for Linear MDPs
Kazumune Hashimoto, Shunki Kimura, Kazunobu Serizawa +3
We study data-driven computation of probabilistic controlled invariant sets (PCIS) for safety-critical reinforcement learning under unknown dynamics. Assuming a linear MDP model, w…
Soft Switching Expert Policies for Controlling Systems with Uncertain Parameters
Junya Ikemoto
This paper proposes a simulation-based reinforcement learning algorithm for controlling systems with uncertain and varying system parameters. While simulators are useful for safely…
Deep Reinforcement Learning Based Networked Control with Network Delays for Signal Temporal Logic Specifications
Junya Ikemoto, Toshimitsu Ushio
We apply deep reinforcement learning (DRL) to design of a networked controller with network delays to complete a temporal control task that is described by a signal temporal logic…
Model-free Control of Chaos with Continuous Deep Q-learning
Junya Ikemoto, Toshimitsu Ushio
The OGY method is one of control methods for a chaotic system. In the method, we have to calculate a stabilizing periodic orbit embedded in its chaotic attractor. Thus, we cannot u…