arXiv subjects

Andreas A. Malikopoulos

Publications and source records attributed to Andreas A. Malikopoulos.

At least 37 records · Page 2Linked to original sources

arXiv2026

Rollout-Based Charging Scheduling for Electric Truck Fleets in Large Transportation Networks

In this paper, we investigate the charging scheduling optimization problem for large electric truck fleets operating with dedicated charging infrastructure. A central coordinator jointly determines the charging sequence and power allocation of each truck to minimize the total operational cost of the fleet. The problem is inherently combinatorial and nonlinear due to the coupling between discrete sequencing decisions and continuous charging control, rendering exact optimization intractable for real-time implementation. To address this challenge, we propose a rollout-based dynamic programming framework built upon an inner-outer two-layer structure, which decouples ordering decisions from the schedule optimization, thus enabling efficient policy evaluation and approximation. The proposed method achieves near-optimal solutions with polynomial-time complexity and adapts to dynamic arrivals and time-varying electricity prices. Simulation studies show that the rollout-based approach significantly outperforms conventional heuristics with high computational efficiency, demonstrating its effectiveness and practical applicability for real-time charging management in large-scale transportation networks.

eess.SY↗

arXiv2026

Cooperative Detour Planning for Dual-Task Drone Fleets

As Urban air mobility scales, commercial drone fleets offer a compelling, yet underexplored opportunity to function as mobile sensor networks for real-time urban traffic monitoring. In this paper, we propose a decentralized framework that enables drone fleets to simultaneously execute delivery tasks and observe network traffic conditions. We model the urban environment with dynamic information values associated with road segments, which accumulate traffic condition uncertainty over time and are reset upon drone visitation. This problem is formulated as a mixed-integer linear programming problem where drones maximize the traffic information reward while respecting the maximum detour for each delivery and the battery budget of each drone. Unlike centralized approaches that are computationally heavy for large fleets, our method focuses on dynamic local clustering. When drones enter communication range, they exchange their belief in traffic status and transition from isolated path planning to a local joint optimization mode, resolving coupled constraints to obtain replanned paths for each drone, respectively. Simulation results built on the real city network of Barcelona, Spain, demonstrate that, compared to a shortest-path policy that ignores the traffic monitoring task, our proposed method better utilizes the battery and detour budget to explore the city area and obtain adequate traffic information; and, thanks to its decentralized manner, this ``meet-and-merge" strategy achieves near-global optimality in network coverage with significantly reduced computation overhead compared to the centralized baseline.

eess.SY↗

arXiv2026

Communication-Efficient Distributed Learning with Differential Privacy

We address nonconvex learning problems over undirected networks. In particular, we focus on the challenge of designing an algorithm that is both communication-efficient and that guarantees the privacy of the agents' data. The first goal is achieved through a local training approach, which reduces communication frequency. The second goal is achieved by perturbing gradients during local training, specifically through gradient clipping and additive noise. We prove that the resulting algorithm converges to a stationary point of the problem within a bounded distance. Additionally, we provide theoretical privacy guarantees within a differential privacy framework that ensure agents' training data cannot be inferred from the trained model shared over the network. We show the algorithm's superior performance on a classification task under the same privacy budget, compared with state-of-the-art methods.

cs.LG↗

arXiv2026

A Functional Learning Approach for Team-Optimal Traffic Coordination

In this paper, we develop a kernel-based policy iteration functional learning framework for computing team-optimal strategies in traffic coordination problems. We consider a multi-agent discrete-time linear system with a cost function that combines quadratic regulation terms and nonlinear safety penalties. Building on the Hilbert space formulation of offline receding-horizon policy iteration, we seek approximate solutions within a reproducing kernel Hilbert space, where the policy improvement step is implemented via a discrete Fréchet derivative. We further study the model-free receding-horizon scenario, where the system dynamics are estimated using recursive least squares, followed by updating the policy using rolling online data. The proposed method is tested in signal-free intersection scenarios via both model-based and model-free simulations and validated in SUMO.

eess.SY↗

arXiv2026

Adaptive Mitigation of Insider Threats via Off-Policy Learning

An insider is a team member who covertly deviates from the team's optimal collaborative strategy to pursue a private objective while still appearing cooperative. Such an insider may initially behave cooperatively but later switch to selfish or malicious actions, thereby degrading collective performance, threatening mission success, and compromising operational safety. In this paper, we study such insider threats within an insider-aware, game-theoretic formulation, where the insider interacts with a decision maker (DM) under a continuous-time switched system, with each time interval characterized by a distinct insider behavioral pattern or threat level. We develop a periodic off-policy mitigation scheme that enables the DM to learn optimal mitigation policies from online data when encountering different insider threats, without requiring a priori knowledge of insider intentions. By designing appropriate conditions on the inter-learning interval time, we establish convergence guarantees for both the learning process and the closed-loop system, and characterize the corresponding mitigation performance achieved by the DM.

math.OC↗

arXiv2026

A Coordinated Routing Approach for Enhancing Bus Timeliness and Travel Efficiency in Mixed-Traffic Environment

This paper proposes a coordinated routing approach that investigates the use of connected and automated vehicles (CAVs) in dedicated bus lanes. The aim is to improve bus schedule adherence while enhancing the travel efficiency of CAVs during the transitional phase of mixed traffic environments. Our approach utilizes real-time traffic data to dynamically reroute CAVs in anticipation of congestion. By continuously monitoring traffic conditions on dedicated lanes and tracking the real-time positions of buses, the system adjusts CAV routes in advance to avoid potential interference with operating buses. This cooperation reduces CAV travel times and minimizes delays that impact transit services. The proposed strategy is validated using microscopic traffic simulations in SUMO. The results demonstrate significant improvements in both transit on-time performance and CAV travel efficiency across a range of traffic conditions.

eess.SY↗

arXiv2026

Battery Discharge Modeling for Electric Vehicles: A Hybrid Physics-based Residual Learning Approach

The growing integration of electric vehicle (EV) fleets into transportation services and energy systems requires accurate modeling of battery discharge and state-of-charge (SoC) evolution to ensure reliable vehicle operation and grid coordination. Existing approaches face a trade-off between interpretable but simplified physics-based models and data-driven methods that demand large datasets and may lack physical consistency. In this paper, we propose a hybrid physics-based residual learning framework for EV battery discharge modeling. A vehicle dynamics model based on force-balance equations provides an interpretable baseline estimate of energy consumption and SoC evolution, capturing aerodynamic drag, rolling resistance, and regenerative braking. A neural network residual learner then corrects discrepancies caused by complex factors such as traffic conditions and driver behavior. Experimental results on $1,500$ trip scenarios demonstrate that the proposed approach reduces the mean absolute percentage error to approximately $0.8\%$, significantly outperforming physics-only models while preserving physical interpretability and computational efficiency.

eess.SY↗

arXiv2026

Predictive Lane-Change and Routing Coordination in Bus-Priority Mixed Traffic Corridors

In this paper, we investigate the coordination of vehicle maneuvers in mixed-traffic corridors where connected and automated vehicles, human-driven vehicles, and buses interact under dedicated bus lane operations. We develop a segment-based network coordination framework that jointly optimizes lane-change and routing decisions of connected and automated vehicles to improve dedicated lane utilization while preserving bus priority. The proposed framework incorporates a predictive bus-protection mechanism that restricts vehicle access to protected lane segments within a monitoring horizon, together with a utility-driven lane-change strategy that accounts for anticipated travel time gains, downstream routing feasibility, and lane-change stability. By explicitly coupling network-level routing decisions with lane-level interaction control, the method proactively mitigates conflicts on dedicated lanes before congestion effects materialize. The proposed approach is evaluated through microscopic traffic simulations in SUMO using a realistic urban corridor. Simulation results demonstrate that the framework enhances bus schedule adherence and reduces average travel times for both automated and human-driven vehicles, while maintaining stable lane-change behavior without increasing maneuver frequency.

eess.SY↗

arXiv2026

When an Approximate Model Suffices for Optimal Control

In this paper, we develop an optimal control framework for dynamical systems when only an approximate model of the underlying plant is available. We consider a setting in which the control strategy is synthesized using a model-based optimal control problem that includes a penalty term capturing deviation from the plant trajectory, while the same control input is applied to both the model and the actual system. For a general class of optimal control problems, we establish conditions under which the control minimizing the model-based Hamiltonian coincides with the plant-optimal control, despite mismatch between the model and the true dynamics. We further specialize these results to problems with quadratic control effort, where explicit and easily verifiable sufficient conditions guarantee equivalence and uniqueness of the resulting optimal control. These results show that accurate control synthesis does not require an exact model of the underlying system, but rather alignment of the optimality conditions that govern control selection. From a learning perspective, this suggests that data-driven efforts can focus on identifying regimes in which model-based and plant-based Hamiltonian minimizers coincide, thereby providing a theoretical basis for robust model-based decision making and the effective use of digital twins under modeling error. We provide examples to illustrate the theoretical findings and demonstrate equivalence of the resulting control trajectories even in the presence of significant model mismatch.

math.OC↗

arXiv2025

Model-Based Reinforcement Learning Under Confounding

We investigate model-based reinforcement learning in contextual Markov decision processes (C-MDPs) in which the context is unobserved and induces confounding in the offline dataset. In such settings, conventional model-learning methods are fundamentally inconsistent, as the transition and reward mechanisms generated under a behavioral policy do not correspond to the interventional quantities required for evaluating a state-based policy. To address this issue, we adapt a proximal off-policy evaluation approach that identifies the confounded reward expectation using only observable state-action-reward trajectories under mild invertibility conditions on proxy variables. When combined with a behavior-averaged transition model, this construction yields a surrogate MDP whose Bellman operator is well defined and consistent for state-based policies, and which integrates seamlessly with the maximum causal entropy (MaxCausalEnt) model-learning framework. The proposed formulation enables principled model learning and planning in confounded environments where contextual information is unobserved, unavailable, or impractical to collect.

cs.LG↗

arXiv2025

Distributed Traffic Signal Control of Interconnected Intersections: A Two-Lane Traffic Network Model

In this paper, we investigate traffic signal control in a network of interconnected intersections, aiming to balance lane-level vehicle densities through optimal green-time allocation. We develop a two-lane traffic flow model that explicitly captures lane-specific propagation dynamics, addressing key limitations of conventional road-level formulations. The proposed model offers a more granular and flexible representation of urban traffic, enabling controllers to react more accurately to lane-specific congestion patterns. Building on this model, we design a distributed model predictive control (MPC) framework and integrate it with the efficient alternating direction method of multipliers (ADMM) to enhance scalability and real-time performance. To accommodate time-varying traffic conditions, we further introduce a data-driven method for forecasting dynamic split ratios. Comprehensive VISSIM simulations on a six-intersection network in Dalian, China, demonstrate that the proposed approach outperforms existing signal control strategies in both traffic efficiency and computational speed, showing its promise for real-time deployment.

eess.SY↗

arXiv2025

Optimal Platoon Formation and Stable Benefit Allocation in Mixed-Energy Truck Fleets under Size Limitations

In this paper, we investigate cooperative platoon formation and benefit allocation in mixed-energy truck fleets composed of both electric and fuel-powered trucks. The central challenge arises from the platoon-size constraint, which limits the number of trucks permitted in each platoon and introduces combinatorial coupling into the search for optimal platoon formation structures. We formulate this problem as a coalitional game with bounded coalition sizes and derive a closed-form characterization of the optimal coalition structure that maximizes the fleet-wide platooning benefit. Building on this structure, we develop a type-based least-core payoff allocation scheme that guarantees stability within the coalition-structure core (CS-core). For cases in which the CS-core is empty, we compute the least-core radius to determine the minimal relaxation required to achieve approximate stability. Through numerical studies, we demonstrate that the proposed framework consistently achieves the highest total platooning benefit among all feasible formation configurations while providing stable benefit allocations that outperform existing baseline methods.

eess.SY↗

arXiv2025

A Hybrid Physics-Based and Reinforcement Learning Framework for Electric Vehicle Charging Time Prediction

In this paper, we develop a hybrid prediction framework for accurate electric vehicle (EV) charging time estimation, a capability that is critical for trip planning, user satisfaction, and efficient operation of charging infrastructure. We combine a physics-based analytical model with a reinforcement learning (RL) approach. The analytical component captures the nonlinear constant-current/constant-voltage (CC--CV) charging dynamics and explicitly models state-of-health (SoH)--dependent capacity and power fade, providing a reliable baseline when historical data are limited. Building on this foundation, we introduce an RL component that progressively refines charging-time predictions as operational data accumulate, enabling improved long-term adaptation. Both models incorporate SoH degradation to maintain predictive accuracy over the battery lifetime. We evaluate the framework using $5{,}000$ simulated charging sessions calibrated to manufacturer specifications and publicly available EV charging datasets. Our results show that the analytical model achieves $R^{2}=98.5\%$ and $\mathrm{MAPE}=2.1\%$, while the RL model further improves performance to $R^{2}=99.2\%$ and $\mathrm{MAPE}=1.6\%$, corresponding to a $23\%$ accuracy gain and $35\%$ improved robustness to battery aging.

eess.SY↗

arXiv2025

Game-Theoretic Learning-Based Mitigation of Insider Threats

An insider is defined as a team member who covertly deviates from the team's optimal collaborative control strategy in pursuit of a private objective, while maintaining an outward appearance of cooperation. Such insider threats can severely undermine cooperative systems: subtle deviations may degrade collective performance, jeopardize mission success, and compromise operational safety. This paper presents a comprehensive framework for identifying and mitigating insider threats in cooperative control settings. We introduce an insider-aware, game-theoretic formulation in which the insider's hidden intention is parameterized, allowing the threat identification task to be reformulated as a parameter estimation problem. To address this challenge, we employ an online indirect dual adaptive control approach that simultaneously infers the insider's control strategy and counteracts its negative influence. By injecting properly designed probing signals, the resulting mitigation policy asymptotically recovers the nominal optimal control law - one that would be achieved under full knowledge of the insider's objective. Simulation results validate the effectiveness of the proposed identification-mitigation framework and illustrate its capability to preserve team performance even in the presence of covert adversarial behavior.

math.OC↗

arXiv2025

Learning-Based Robust Bayesian Persuasion with Conformal Prediction Guarantees

Classical Bayesian persuasion assumes that senders fully understand how receivers form beliefs and make decisions--an assumption that rarely holds when receivers possess private information or exhibit non-Bayesian behavior. In this paper, we develop a learning-based framework that integrates neural networks with conformal prediction to achieve robust persuasion under uncertainty about receiver belief formation. The proposed neural architecture learns end-to-end mappings from receiver observations and sender signals to action predictions, eliminating the need to identify belief mechanisms explicitly. Conformal prediction constructs finite-sample valid prediction sets with provable marginal coverage, enabling principled, distribution-free robust optimization. We establish exact coverage guarantees for the data-generating policy and derive bounds on coverage degradation under policy shifts. Furthermore, we provide neural network approximation and estimation error bounds, with sample complexity $O(d \log(|\mathcal{U}||\mathcal{Y}||\mathcal{S}|)/\varepsilon^2)$, where $d$ denotes the effective network dimension, and finite-sample lower bounds on the sender's expected utility. Numerical experiments on smart-grid energy management illustrate the framework's robustness.

eess.SY↗

arXiv2025

Actor-Critic Learning for Risk-Constrained Linear Quadratic Regulation

In this paper, we investigate the infinite-horizon risk-constrained linear quadratic regulator problem (RC-QR), which augments the classical LQR formulation with a statistical constraint on the variability of the system state to incorporate risk awareness, a key requirement in safety-critical control applications. We propose an actor-critic learning algorithm that jointly performs policy evaluation and policy improvement in a model-free and online manner. The RC-QR problem is first reformulated as a max-min optimization problem, from which we develop a multi-time-scale stochastic approximation scheme. The critic employs temporal-difference learning to estimate the action-value function, the actor updates the policy parameters via a policy gradient step, and the dual variable is adapted through gradient ascent to enforce the risk constraint.

math.OC↗

arXiv2025

A Communication-Efficient Decentralized Actor-Critic Algorithm

In this paper, we study the problem of reinforcement learning in multi-agent systems where communication among agents is limited. We develop a decentralized actor-critic learning framework in which each agent performs several local updates of its policy and value function, where the latter is approximated by a multi-layer neural network, before exchanging information with its neighbors. This local training strategy substantially reduces the communication burden while maintaining coordination across the network. We establish finite-time convergence analysis for the algorithm under Markov-sampling. Specifically, to attain the $\varepsilon$-accurate stationary point, the sample complexity is of order $\mathcal{O}(\varepsilon^{-3})$ and the communication complexity is of order $\mathcal{O}(\varepsilon^{-1}τ^{-1})$, where tau denotes the number of local training steps. We also show how the final error bound depends on the neural network's approximation quality. Numerical experiments in a cooperative control setting illustrate and validate the theoretical findings.

cs.LG↗

arXiv2025

Combined Learning and Control: A New Paradigm for Optimal Control with Unknown Dynamics

In this paper, we present the combined learning-and-control (CLC) approach, which is a new way to solve optimal control problems with unknown dynamics by unifying model-based control and data-driven learning. The key idea is simple: we design a controller to be optimal for a proxy objective built on an available model while penalizing mismatches with the real system, so that the resulting controller is also optimal for the actual system. Building on the original CLC formulation, we demonstrate the framework to the linear quadratic regulator problem and make three advances: (i) we show that the CLC penalty is a sequence of stage-specific weights rather than a single constant; (ii) we identify when these weights can be set in advance and when they must depend on the (unknown) dynamics; and (iii) we develop a lightweight learning loop that tunes the weights directly from data without abandoning the benefits of a model-based design. We provide a complete algorithm and an empirical study against common baseline methods. The results clarify where prior knowledge suffices and where learning is essential, and they position CLC as a practical, theoretically grounded bridge between classical optimal control and modern learning methods.

eess.SY↗