Challenges and opportunities in deep reinforcement learning with graph neural networks: A comprehensive review of algorithms and applications
Deep reinforcement learning (DRL) has empowered a variety of artificial intelligence fields,
including pattern recognition, robotics, recommendation systems, and gaming. Similarly …
including pattern recognition, robotics, recommendation systems, and gaming. Similarly …
Event-triggered deep reinforcement learning using parallel control: A case study in autonomous driving
This paper utilizes parallel control to investigate the problem of event-triggered deep
reinforcement learning and develops an event-triggered deep Q-network (ETDQN) for …
reinforcement learning and develops an event-triggered deep Q-network (ETDQN) for …
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 …
Deep reinforcement learning for wireless sensor scheduling in cyber–physical systems
In many cyber–physical systems, we encounter the problem of remote state estimation of
geographically distributed and remote physical processes. This paper studies the …
geographically distributed and remote physical processes. This paper studies the …
Continuous-discrete reinforcement learning for hybrid control in robotics
Many real-world control problems involve both discrete decision variables–such as the
choice of control modes, gear switching or digital outputs–as well as continuous decision …
choice of control modes, gear switching or digital outputs–as well as continuous decision …
Machine learning in event-triggered control: Recent advances and open issues
Networked control systems have gained considerable attention over the last decade as a
result of the trend towards decentralised control applications and the emergence of cyber …
result of the trend towards decentralised control applications and the emergence of cyber …
Event-triggered model predictive control with deep reinforcement learning for autonomous driving
Event-triggered model predictive control (eMPC) is a popular optimal control method with an
aim to alleviate the computation and/or communication burden of MPC. However, it …
aim to alleviate the computation and/or communication burden of MPC. However, it …
Deep learning for wireless networked systems: A joint estimation-control-scheduling approach
Wireless-networked control system (WNCS) connecting sensors, controllers, and actuators
via wireless communications is a key enabling technology for highly scalable and low-cost …
via wireless communications is a key enabling technology for highly scalable and low-cost …
Deep reinforcement learning of event-triggered communication and consensus-based control for distributed cooperative transport
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 …
Optimizing data center energy efficiency via event-driven deep reinforcement learning
Y Ran, X Zhou, H Hu, Y Wen - IEEE Transactions on Services …, 2022 - ieeexplore.ieee.org
To reduce the skyrocketing energy consumption of data centers, the prevailing approaches
adopt the time-driven manner to control IT and cooling subsystems. These methods suffer …
adopt the time-driven manner to control IT and cooling subsystems. These methods suffer …