13 citations · 16 across the 9 of their papers we have counts for
9 papers
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…
Structural-Ambiguity-Aware Translation from Natural Language to Signal Temporal Logic
Kosei Fushimi, Kazunobu Serizawa, Junya Ikemoto +1
Signal Temporal Logic (STL) is widely used to specify timed and safety-critical tasks for cyber-physical systems, but writing STL formulas directly is difficult for non-expert user…
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 under signal temporal logic constraints using Lagrangian relaxation
Junya Ikemoto, Toshimitsu Ushio
Deep reinforcement learning (DRL) has attracted much attention as an approach to solve optimal control problems without mathematical models of systems. On the other hand, in genera…
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…
Continuous Deep Q-Learning with Simulator for Stabilization of Uncertain Discrete-Time Systems
Junya Ikemoto, Toshimitsu Ushio
Applications of reinforcement learning (RL) to stabilization problems of real systems are restricted since an agent needs many experiences to learn an optimal policy and may determ…
Networked Control of Nonlinear Systems under Partial Observation Using Continuous Deep Q-Learning
Junya Ikemoto, Toshimitsu Ushio
In this paper, we propose a design of a model-free networked controller for a nonlinear plant whose mathematical model is unknown. In a networked control system, the controller and…
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…