Marabou 2.0: a versatile formal analyzer of neural networks
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Event-triggered communication network with limited-bandwidth constraint for multi-agent reinforcement learning
Communicating agents with each other in a distributed manner and behaving as a group are
essential in multi-agent reinforcement learning. However, real-world multi-agent systems …
essential in multi-agent reinforcement learning. However, real-world multi-agent systems …
[HTML][HTML] Wireless control: Retrospective and open vistas
The convergence of wireless networks and control engineering has been a technological
driver since the beginning of this century. It has significantly contributed to a wide set of …
driver since the beginning of this century. It has significantly contributed to a wide set of …
Deep reinforcement learning of event-triggered communication and consensus-based control for distributed cooperative transport
K Shibata, T Jimbo, T Matsubara - Robotics and Autonomous Systems, 2023 - Elsevier
In this paper, we present a solution to a design problem of control strategies for multi-agent
cooperative transport. Although existing learning-based methods assume that the number of …
cooperative transport. Although existing learning-based methods assume that the number of …
Event-triggered learning
F Solowjow, S Trimpe - Automatica, 2020 - Elsevier
The efficient exchange of information is an essential aspect of intelligent collective behavior.
Event-triggered control and estimation achieve some efficiency by replacing continuous data …
Event-triggered control and estimation achieve some efficiency by replacing continuous data …
Experimental study of transport layer protocols for wireless networked control systems
In Wireless Networked Control Systems (WNCSs), the feedback control loops are closed
over a wireless communication network. The proliferation of WNCSs requires efficient …
over a wireless communication network. The proliferation of WNCSs requires efficient …
Model‐free self‐triggered control based on deep reinforcement learning for unknown nonlinear systems
H Wan, HR Karimi, X Luan, F Liu - International Journal of …, 2023 - Wiley Online Library
This article proposes a joint learning technique for control inputs and triggering intervals of
self‐triggered control nonlinear systems with unknown dynamics. First, deep reinforcement …
self‐triggered control nonlinear systems with unknown dynamics. First, deep reinforcement …
Recursive reasoning with reduced complexity and intermittency for nonequilibrium learning in stochastic games
F Fotiadis, KG Vamvoudakis - IEEE Transactions on Neural …, 2022 - ieeexplore.ieee.org
In this article, we propose a computationally and communicationally efficient approach for
decision-making in nonequilibrium stochastic games. In particular, due to the inherent …
decision-making in nonequilibrium stochastic games. In particular, due to the inherent …
Deep reinforcement learning of event-triggered communication and control for multi-agent cooperative transport
K Shibata, T Jimbo, T Matsubara - 2021 IEEE International …, 2021 - ieeexplore.ieee.org
In this paper, we explore a multi-agent reinforcement learning approach to address the
design problem of communication and control strategies for multi-agent cooperative …
design problem of communication and control strategies for multi-agent cooperative …
Toward multi-agent reinforcement learning for distributed event-triggered control
Event-triggered communication and control provide high control performance in networked
control systems without overloading the communication network. However, most …
control systems without overloading the communication network. However, most …