Modeling Gait Using CPG (Central Pattern Generator) and Neural Network. - Jalal Arabneydi; Behzad Moshiri; Fariba Bahrami (COST 2101/2102 Conference, 2009)

Team optimal control of coupled subsystems with mean-field sharing. - Jalal Arabneydi; Aditya Mahajan (CDC, 2014)

Reinforcement learning in decentralized stochastic control systems with partial history sharing. - Jalal Arabneydi; Aditya Mahajan (ACC, 2015)

Team-optimal solution of finite number of mean-field coupled LQG subsystems. - Jalal Arabneydi; Aditya Mahajan (CDC, 2015)

A certainty equivalence result in team-optimal control of mean-field coupled Markov chains. - Jalal Arabneydi; Amir G. Aghdam (CDC, 2017)

Optimal Dynamic Pricing for Binary Demands in Smart Grids: A Fair and Privacy-Preserving Strategy. - Jalal Arabneydi; Amir G. Aghdam (ACC, 2018)

Optimal Distributed Control for Leader-Follower Networks: A Scalable Design. - Jalal Arabneydi; Mohammad M. Baharloo; Amir G. Aghdam (CCECE, 2018)

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 (CDC, 2018)

Near-Optimal Design for Fault-Tolerant Systems with Homogeneous Components under Incomplete Information. - Jalal Arabneydi; Amir G. Aghdam (MWSCAS, 2018)

A Mean-Field Team Approach to Minimize the Spread of Infection in a Network. - Jalal Arabneydi; Amir G. Aghdam (ACC, 2019)

Data Collection versus Data Estimation: A Fundamental Trade-off in Dynamic Invariant Networks with Known and Unknown Models. - Jalal Arabneydi (IEEE DataPort, 2019)

Data Collection versus Data Estimation: A Fundamental Trade-off in Dynamic Invariant Networks with Known and Unknown Models. - Jalal Arabneydi (IEEE DataPort, 2019)

Reinforcement Learning in Deep Structured Teams: Initial Results with Finite and Infinite Valued Features. - Jalal Arabneydi; Masoud Roudneshin; Amir G. Aghdam (CCTA, 2020)

Reinforcement Learning in Linear Quadratic Deep Structured Teams: Global Convergence of Policy Gradient Methods. - Vida Fathi; Jalal Arabneydi; Amir G. Aghdam (CDC, 2020)

Reinforcement Learning in Nonzero-sum Linear Quadratic Deep Structured Games: Global Convergence of Policy Optimization. - Masoud Roudneshin; Jalal Arabneydi; Amir G. Aghdam (CDC, 2020)

MinMax Mean-Field Team Approach for a Leader-Follower Network: A Saddle-Point Strategy. - Mohammad M. Baharloo; Jalal Arabneydi; Amir G. Aghdam (IEEE Control. Syst. Lett., 2020)

Deep Teams: Decentralized Decision Making With Finite and Infinite Number of Agents. - Jalal Arabneydi; Amir G. Aghdam (IEEE Trans. Autom. Control., 2020)

Data Collection Versus Data Estimation: A Fundamental Trade-Off in Dynamic Networks. - Jalal Arabneydi; Amir G. Aghdam (IEEE Trans. Netw. Sci. Eng., 2020)

Receding Horizon Control in Deep Structured Teams: A Provably Tractable Large-Scale Approach with Application to Swarm Robotics. - Jalal Arabneydi; Amir G. Aghdam (CDC, 2021)

Deep Structured Teams in Arbitrary-Size Linear Networks: Decentralized Estimation, Optimal Control and Separation Principle. - Jalal Arabneydi; Amir G. Aghdam (CDC, 2021)

A Systematic Approach to Design Real-World Human-in-the-Loop Deep Reinforcement Learning: Salient Features, Challenges and Trade-offs. - Jalal Arabneydi; Md. Saiful Islam 007; Srijita Das 001; Sai Krishna Gottipati; William Duguay; Clodéric Mars; Matthew E. Taylor; Matthew Guzdial; Antoine Fagette; Younes Zerouali (CoRR, 2025)

Human-AI collaboration in real-world complex environment with reinforcement learning. - Md. Saiful Islam 007; Srijita Das 001; Sai Krishna Gottipati; William Duguay; Clodéric Mars; Jalal Arabneydi; Antoine Fagette; Matthew Guzdial; Matthew E. Taylor (Neural Comput. Appl., 2025)