87 citations · 132 across the 27 of their papers we have counts for
10 papers · 1 filter
Reinforcement Learning in Linear Quadratic Deep Structured Teams: Global Convergence of Policy Gradient Methods
Vida Fathi, Jalal Arabneydi, Amir G. Aghdam
In this paper, we study the global convergence of model-based and model-free policy gradient descent and natural policy gradient descent algorithms for linear quadratic deep struct…
Reinforcement Learning in Nonzero-sum Linear Quadratic Deep Structured Games: Global Convergence of Policy Optimization
Masoud Roudneshin, Jalal Arabneydi, Amir G. Aghdam
We study model-based and model-free policy optimization in a class of nonzero-sum stochastic dynamic games called linear quadratic (LQ) deep structured games. In such games, player…
MinMax Mean-Field Team Approach for a Leader-Follower Network: A Saddle-Point Strategy
Mohammad M. Baharloo, Jalal Arabneydi, Amir G. Aghdam
This paper investigates a soft-constrained MinMax control problem of a leader-follower network. The network consists of one leader and an arbitrary number of followers that wish to…
A Certainty Equivalence Result in Team-Optimal Control of Mean-Field Coupled Markov Chains
Jalal Arabneydi, Amir G. Aghdam
This paper studies a large number of homogeneous Markov decision processes where the transition probabilities and costs are coupled in the empirical distribution of states (also ca…
Optimal Dynamic Pricing for Binary Demands in Smart Grids: A Fair and Privacy-Preserving Strategy
Jalal Arabneydi, Amir G. Aghdam
Motivated by demand-side management in smart grids, a decentralized controlled Markov chain formulation is proposed to model a homogeneous population of users with binary demands (…
Near-Optimal Design for Fault-Tolerant Systems with Homogeneous Components under Incomplete Information
Jalal Arabneydi, Amir G. Aghdam
In this paper, we study a fault-tolerant control for systems consisting of multiple homogeneous components such as parallel processing machines. This type of system is often more r…