Publications de Maryam Kamgarpour

Learning to Assemble with Alternative Plans

Maryam Kamgarpour, Stefana Parascho, Gabriel Rémi Vallat

We present a reinforcement learning framework for constructing assemblies composed of rigid parts, which are commonly seen in many historical masonry buildings and bridges. Traditional construction methods for such structures often depend on dense scaffold ...

Association for Computing Machinery (ACM)2025

A learning-based approach to stochastic optimal control under reach-avoid constraint

Maryam Kamgarpour

We develop a model-free approach to optimally control stochastic, Markovian systems subject to a reach-avoid constraint. Specifically, the state trajectory must remain within a safe set while reaching a target set within a finite time horizon. Due to the t ...

Association for Computing Machinery2025

On the Convergence of Gradient Descent in Scalar Two-Agent Infinite-Horizon LQ Games

Maryam Kamgarpour

Institute of Electrical and Electronics Engineers (IEEE)2025

Chance-Constrained Linear Quadratic Gaussian Games for Multi-Robot Interaction Under Uncertainty

Maryam Kamgarpour, Kai Ren

Institute of Electrical and Electronics Engineers (IEEE)2025

Safe optimization with grey-box information: Application to composites autoclave processing improvement on the fly

Maryam Kamgarpour

In the manufacture of aerospace-grade composites in the autoclave, the curing process plays a crucial role as it directly governs the quality of the final parts. Maintaining the part's thermal history, namely, thermal lag and exotherm, under predetermined ...

2025

Convergence of a model-free entropy-regularized inverse reinforcement learning algorithm

Maryam Kamgarpour

Given a dataset of expert demonstrations, inverse reinforcement learning (IRL) aims to recover a reward for which the expert is optimal. This work proposes a model-free algorithm to solve the entropy-regularized IRL problem. In particular, we employ a stoc ...

Institute of Electrical and Electronics Engineers Inc.2024

Payoff-Based Learning of Nash Equilibria in Merely Monotone Games

Maryam Kamgarpour

We address learning Nash equilibria in convex games under the payoff information setting. We consider the case in which the game pseudo- gradient is monotone but not necessarily strictly monotone. This relaxation of strict monotonicity enables application ...

Institute of Electrical and Electronics Engineers (IEEE)2024

Aircraft Trajectory Planning for Climate Hotspot Avoidance Considering Air Traffic Complexity: A Constrained Multi-Agent Reinforcement Learning Approach

Maryam Kamgarpour

Planning aircraft trajectories to avoid climate-sensitive areas poses operational challenges, including increased traffic complexity and potential safety risks. This study presents a framework designed to plan operationally feasible climate-friendly routes ...

2024

Multi-Agent Learning in Contextual Games under Unknown Constraints

Maryam Kamgarpour, Anna Maria Maddux

We consider the problem of learning to play a repeated contextual game with unknown reward and unknown constraints functions. Such games arise in applications where each agent’s action needs to belong to a feasible set, but the feasible set is a priori unk ...

2024

Learning to bid in forward electricity markets using a no-regret algorithm

Maryam Kamgarpour

It is a common practice in the current literature of electricity markets to use game-theoretic approaches for strategic price bidding. However, they generally rely on the assumption that the strategic bidders have prior knowledge of rival bids, either perf ...

Elsevier2024