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Found 7 papers, 2 papers with code
Zeroth-Order Feedback-Based Optimization for Distributed Demand Response
Distributed demand response is a typical distributed optimization problem that requires coordination among multiple agents to satisfy demand response requirements.
Beef up mmWave Dense Cellular Networks with D2D-Assisted Cooperative Edge Caching
In this paper, we propose a device-to-device (D2D) assisted cooperative edge caching (DCEC) policy for millimeter (mmWave) dense networks, which cooperatively utilizes the cache resource of users and SBSs in proximity.
Communication-Efficient Distributed SGD with Compressed Sensing
We consider large scale distributed optimization over a set of edge devices connected to a central server, where the limited communication bandwidth between the server and edge devices imposes a significant bottleneck for the optimization procedure.
Reinforcement Learning for Orientation Estimation Using Inertial Sensors with Performance Guarantee
This paper presents a deep reinforcement learning (DRL) algorithm for orientation estimation using inertial sensors combined with magnetometer.
Analysis of the Optimization Landscape of Linear Quadratic Gaussian (LQG) Control
This paper revisits the classical Linear Quadratic Gaussian (LQG) control from a modern optimization perspective.
Policy Gradient Methods
Optimization and Control
Systems and Control
Systems and Control
Dynamical Systems
Reinforcement Learning for Selective Key Applications in Power Systems: Recent Advances and Future Challenges
With large-scale integration of renewable generation and distributed energy resources, modern power systems are confronted with new operational challenges, such as growing complexity, increasing uncertainty, and aggravating volatility.
Distributed Reinforcement Learning for Decentralized Linear Quadratic Control: A Derivative-Free Policy Optimization Approach
We propose a Zero-Order Distributed Policy Optimization algorithm (ZODPO) that learns linear local controllers in a distributed fashion, leveraging the ideas of policy gradient, zero-order optimization and consensus algorithms.
