29 citations · 78 across the 18 of their papers we have counts for
19 papers
Learning-based Bounded Synthesis for Semi-MDPs with LTL Specifications
Ryohei Oura, Toshimitsu Ushio
This letter proposes a learning-based bounded synthesis for a semi-Markov decision process (SMDP) with a linear temporal logic (LTL) specification. In the product of the SMDP and t…
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…
Collaborative rover-copter path planning and exploration with temporal logic specifications based on Bayesian update under uncertain environments
Kazumune Hashimoto, Natsuko Tsumagari, Toshimitsu Ushio
This paper investigates a collaborative rover-copter path planning and exploration with temporal logic specifications under uncertain environments. The objective of the rover is to…
Bounded Synthesis and Reinforcement Learning of Supervisors for Stochastic Discrete Event Systems with LTL Specifications
Ryohei Oura, Toshimitsu Ushio, Ami Sakakibara
In this paper, we consider supervisory control of stochastic discrete event systems (SDESs) under linear temporal logic specifications. Applying the bounded synthesis, we reduce th…
Stability analysis and control of decision-making of miners in blockchain
Kosuke Toda, Naomi Kuze, Toshimitsu Ushio
To maintain blockchain-based services with ensuring its security, it is an important issue how to decide a mining reward so that the number of miners participating in the mining in…