Search Results for author:
Found 23 papers, 5 papers with code
Efficient Reinforcement Learning for Global Decision Making in the Presence of Local Agents at Scale
This work proposes the SUB-SAMPLE-Q algorithm where the global agent subsamples $k\leq n$ local agents to compute an optimal policy in time that is only exponential in $k$, providing an exponential speedup from standard methods that are exponential in $n$.
Efficient Reinforcement Learning for Routing Jobs in Heterogeneous Queueing Systems
Unlike homogeneous systems, a threshold policy, that routes jobs to the slow server(s) when the queue length exceeds a certain threshold, is known to be optimal for the one-fast-one-slow two-server system.
CoVO-MPC: Theoretical Analysis of Sampling-based MPC and Optimal Covariance Design
Sampling-based Model Predictive Control (MPC) has been a practical and effective approach in many domains, notably model-based reinforcement learning, thanks to its flexibility and parallelizability.
A Scalable Network-Aware Multi-Agent Reinforcement Learning Framework for Decentralized Inverter-based Voltage Control
This paper addresses the challenges associated with decentralized voltage control in power grids due to an increase in distributed generations (DGs).
Compositional Neural Certificates for Networked Dynamical Systems
Specifically, we treat a large networked dynamical system as an interconnection of smaller subsystems and develop methods that can find each subsystem a decentralized controller and an ISS Lyapunov function; the latter can be collectively composed to prove the global stability of the system.
Global Convergence of Localized Policy Iteration in Networked Multi-Agent Reinforcement Learning
In particular, we show that, despite restricting each agent's attention to only its $\kappa$-hop neighborhood, the agents are able to learn a policy with an optimality gap that decays polynomially in $\kappa$.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Stability Constrained Reinforcement Learning for Decentralized Real-Time Voltage Control
In this paper, we propose a stability-constrained reinforcement learning (RL) method for real-time voltage control, that guarantees system stability both during policy learning and deployment of the learned policy.
KCRL: Krasovskii-Constrained Reinforcement Learning with Guaranteed Stability in Nonlinear Dynamical Systems
However, current reinforcement learning (RL) methods lack stabilization guarantees, which limits their applicability for the control of safety-critical systems.
Equipping Black-Box Policies with Model-Based Advice for Stable Nonlinear Control
Machine-learned black-box policies are ubiquitous for nonlinear control problems.
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.
Stability Constrained Reinforcement Learning for Real-Time Voltage Control
Deep reinforcement learning (RL) has been recognized as a promising tool to address the challenges in real-time control of power systems.
Decentralized Graph-Based Multi-Agent Reinforcement Learning Using Reward Machines
Experimental results show that local information is sufficient for DGRM and agents can accomplish complex tasks with the help of RM.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Robustness and Consistency in Linear Quadratic Control with Untrusted Predictions
Motivated by online learning methods, we design a self-tuning policy that adaptively learns the trust parameter $\lambda$ with a competitive ratio that depends on $\varepsilon$ and the variation of system perturbations and predictions.
Stable Online Control of Linear Time-Varying Systems
In this work, we propose an efficient online control algorithm, COvariance Constrained Online Linear Quadratic (COCO-LQ) control, that guarantees input-to-state stability for a large class of LTV systems while also minimizing the control cost.
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.
Learning Optimal Power Flow: Worst-Case Guarantees for Neural Networks
This paper introduces for the first time a framework to obtain provable worst-case guarantees for neural network performance, using learning for optimal power flow (OPF) problems as a guiding example.
Combining Model-Based and Model-Free Methods for Nonlinear Control: A Provably Convergent Policy Gradient Approach
Model-free learning-based control methods have seen great success recently.
Multi-Agent Reinforcement Learning in Stochastic Networked Systems
We study multi-agent reinforcement learning (MARL) in a stochastic network of agents.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Scalable Multi-Agent Reinforcement Learning for Networked Systems with Average Reward
It has long been recognized that multi-agent reinforcement learning (MARL) faces significant scalability issues due to the fact that the size of the state and action spaces are exponentially large in the number of agents.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
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 the (discounted) global reward is maximized.
Finite-Time Analysis of Asynchronous Stochastic Approximation and $Q$-Learning
We consider a general asynchronous Stochastic Approximation (SA) scheme featuring a weighted infinity-norm contractive operator, and prove a bound on its finite-time convergence rate on a single trajectory.
Scalable Reinforcement Learning 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 the (discounted) global reward is maximized.
Exploiting Fast Decaying and Locality in Multi-Agent MDP with Tree Dependence Structure
Further, under some special conditions, we prove that the gap between the approximated reward function and the true reward function is decaying exponentially fast as the length of the truncated Markov process gets longer.
