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 …
modern power systems are confronted with new operational challenges, such as growing …
[PDF][PDF] Reinforcement learning for decision-making and control in power systems: Tutorial, review, and vision
With large-scale integration of renewable generation and distributed energy resources
(DERs), modern power systems are confronted with new operational challenges, such as …
(DERs), modern power systems are confronted with new operational challenges, such as …
Scalable reinforcement learning of localized policies for multi-agent networked systems
We study reinforcement learning (RL) in a setting with a network of agents whose states and
actions interact in a local manner where the objective is to find localized policies such that …
actions interact in a local manner where the objective is to find localized policies such that …
Global convergence of localized policy iteration in networked multi-agent reinforcement learning
We study a multi-agent reinforcement learning (MARL) problem where the agents interact
over a given network. The goal of the agents is to cooperatively maximize the average of …
over a given network. The goal of the agents is to cooperatively maximize the average of …
Neural operators for bypassing gain and control computations in pde backstepping
We introduce a framework for eliminating the computation of controller gain functions in PDE
control. We learn the nonlinear operator from the plant parameters to the control gains with a …
control. We learn the nonlinear operator from the plant parameters to the control gains with a …
Scalable primal-dual actor-critic method for safe multi-agent rl with general utilities
We investigate safe multi-agent reinforcement learning, where agents seek to collectively
maximize an aggregate sum of local objectives while satisfying their own safety constraints …
maximize an aggregate sum of local objectives while satisfying their own safety constraints …
Model-free learning with heterogeneous dynamical systems: A federated LQR approach
We study a model-free federated linear quadratic regulator (LQR) problem where M agents
with unknown, distinct yet similar dynamics collaboratively learn an optimal policy to …
with unknown, distinct yet similar dynamics collaboratively learn an optimal policy to …
Mean-field multiagent reinforcement learning: A decentralized network approach
One of the challenges for multiagent reinforcement learning (MARL) is designing efficient
learning algorithms for a large system in which each agent has only limited or partial …
learning algorithms for a large system in which each agent has only limited or partial …
Sample complexity of asynchronous Q-learning: Sharper analysis and variance reduction
Asynchronous Q-learning aims to learn the optimal action-value function (or Q-function) of a
Markov decision process (MDP), based on a single trajectory of Markovian samples induced …
Markov decision process (MDP), based on a single trajectory of Markovian samples induced …
Near-optimal distributed linear-quadratic regulator for networked systems
This paper studies the trade-off between the degree of decentralization and the performance
of a distributed controller in a linear-quadratic control setting. We study a system of …
of a distributed controller in a linear-quadratic control setting. We study a system of …