21 citations · 63 across the 24 of their papers we have counts for
24 papers
A Systematic Approach to Design Real-World Human-in-the-Loop Deep Reinforcement Learning: Salient Features, Challenges and Trade-offs
Jalal Arabneydi, Saiful Islam, Srijita Das +7
With the growing popularity of deep reinforcement learning (DRL), human-in-the-loop (HITL) approach has the potential to revolutionize the way we approach decision-making problems…
Human-AI Collaboration in Real-World Complex Environment with Reinforcement Learning
Md Saiful Islam, Srijita Das, Sai Krishna Gottipati +6
Recent advances in reinforcement learning (RL) and Human-in-the-Loop (HitL) learning have made human-AI collaboration easier for humans to team with AI agents. Leveraging human exp…
Deep Structured Teams in Arbitrary-Size Linear Networks: Decentralized Estimation, Optimal Control and Separation Principle
Jalal Arabneydi, Amir G. Aghdam
In this article, we introduce decentralized Kalman filters for linear quadratic deep structured teams. The agents in deep structured teams are coupled in dynamics, costs and measur…
Receding Horizon Control in Deep Structured Teams: A Provably Tractable Large-Scale Approach with Application to Swarm Robotics
Jalal Arabneydi, Amir G. Aghdam
In this paper, a deep structured tracking problem is introduced for a large number of decision-makers. The problem is formulated as a linear quadratic deep structured team, where t…
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…
Team Optimal Control of Coupled Major-Minor Subsystems with Mean-Field Sharing
Jalal Arabneydi, Aditya Mahajan
In this paper, we investigate team optimal control of coupled major-minor subsystems with mean-field sharing. In such a model, there is one major subsystem that directly influences…
Team-Optimal Solution of Finite Number of Mean-Field Coupled LQG Subsystems
Jalal Arabneydi, Aditya Mahajan
A decentralized control system with linear dynamics, quadratic cost, and Gaussian disturbances is considered. The system consists of a finite number of subsystems whose dynamics an…
Reinforcement Learning in Decentralized Stochastic Control Systems with Partial History Sharing
Jalal Arabneydi, Aditya Mahajan
In this paper, we are interested in systems with multiple agents that wish to collaborate in order to accomplish a common task while a) agents have different information (decentral…
Team Optimal Control of Coupled Subsystems with Mean-Field Sharing
Jalal Arabneydi, Aditya Mahajan
We investigate team optimal control of stochastic subsystems that are weakly coupled in dynamics (through the mean-field of the system) and are arbitrary coupled in the cost. The c…
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…
Near-Optimal Control Strategy in Leader-Follower Networks: A Case Study for Linear Quadratic Mean-Field Teams
Mohammad M. Baharloo, Jalal Arabneydi, Amir G. Aghdam
In this paper, a decentralized stochastic control system consisting of one leader and many homogeneous followers is studied. The leader and followers are coupled in both dynamics a…
Optimal Distributed Control for Leader-Follower Networks: A Scalable Design
Jalal Arabneydi, Mohammad M. Baharloo, Amir G. Aghdam
The focus of this paper is directed towards optimal control of multi-agent systems consisting of one leader and a number of followers in the presence of noise. The dynamics of ever…
A Mean-Field Team Approach to Minimize the Spread of Infection in a Network
Jalal Arabneydi, Amir G. Aghdam
In this paper, a stochastic dynamic control strategy is presented to prevent the spread of an infection over a homogeneous network. The infectious process is persistent, i.e., it c…
Reinforcement Learning in Deep Structured Teams: Initial Results with Finite and Infinite Valued Features
Jalal Arabneydi, Masoud Roudneshin, Amir G. Aghdam
In this paper, we consider Markov chain and linear quadratic models for deep structured teams with discounted and time-average cost functions under two non-classical information st…
Deep Nash and Sequential Mean-Field Equilibria in Cooperative and Non-cooperative Games with Imperfect Information Structures
Jalal Arabneydi, Amir G. Aghdam
A class of nonzero-sum stochastic dynamic games with imperfect information structure is investigated. The game involves an arbitrary number of players, modeled as homogeneous Marko…
Deep Teams: Decentralized Decision Making with Finite and Infinite Number of Agents
Jalal Arabneydi, Amir G. Aghdam
Inspired by the concepts of deep learning in artificial intelligence and fairness in behavioural economics, we introduce deep teams in this paper. In such systems, agents are parti…
Data Collection versus Data Estimation: A Fundamental Trade-off in Dynamic Networks
Jalal Arabneydi, Amir G. Aghdam
An important question that often arises in the operation of networked systems is whether to collect the real-time data or to estimate them based on the previously collected data. V…
Deep Structured Teams with Linear Quadratic Model: Partial Equivariance and Gauge Transformation
Jalal Arabneydi, Amir G. Aghdam
Motivated by the recent developments in artificial intelligence, we introduce linear quadratic deep structured teams in this paper. Two notions of equivariant and partially equivar…
Explicit Sequential Equilibria in LQ Deep Structured Games and Weighted Mean-Field Games
Jalal Arabneydi, Amir G. Aghdam, Roland P. Malhamé
We investigate a class of nonzero-sum dynamic stochastic games where players have linear dynamics and quadratic costs, coupled in both dynamics and cost through a linear regression…
Linear Quadratic Mean Field Teams: Optimal and Approximately Optimal Decentralized Solutions
Jalal Arabneydi, Aditya Mahajan
We consider team optimal control of decentralized systems with linear dynamics, quadratic costs, and arbitrary disturbance that consist of multiple sub-populations with exchangeabl…