arXiv subjects

Jae Kim

Publications and source records attributed to Jae Kim.

3 recordsLinked to original sources

arXiv2026

Communication-Aware Multi-Agent Reinforcement Learning for Decentralized Cooperative UAV Deployment

Autonomous Unmanned Aerial Vehicle (UAV) swarms are increasingly used as rapidly deployable aerial relays and sensing platforms, yet practical deployments must operate under partial observability and intermittent peer-to-peer connectivity. We present a graph-based multi-agent reinforcement learning framework trained under centralized training with decentralized execution (CTDE): a centralized critic and global state are available only during training, while each UAV executes a shared policy using local observations and messages from nearby neighbors. Under restricted communication, neighbor relations are induced by an SNR-threshold connectivity graph. Our architecture encodes local agent state and nearby entities with an agent-entity attention module and aggregates inter-UAV messages with neighbor self-attention over a signal-quality-limited communication graph defined by a channel model. We evaluate the framework on a cooperative relay-deployment task, DroneConnect. Experimental results show that the proposed method achieves an approximately 12% increase in target coverage over MAPPO under restricted communication and partial observability, while remaining competitive with a mixed-integer linear programming (MILP)-based offline upper bound with full node observability.

cs.MA↗

arXiv2024

Predictable by Design, Vulnerable by Nature: Security Consequences of Learnability in UAV State Estimators

If a mathematical function can be learned from its input/output behavior alone, can an adversary exploit that ``learnability'' to compromise it? What if the function is core to estimating the state of, and controlling, unmanned vehicles such as drones? We investigate this question by targeting state estimators in unmanned aerial vehicles (UAVs) - specifically Extended Kalman Filters (EKFs), an industry standard for autonomous systems, whose inherent modeling of uncertainties and sensor noise create an adversarial space that ML can exploit. We present REQUIEM, a machine-learning based framework for investigating such vulnerabilities. Our framework functions by, (i) constructing deep surrogate models that emulate the state estimation update function using only observed inputs and outputs and (ii) optimizing spoofer models to manipulate sensor values so that they're not easily detectable by standard anomaly detectors - all of which results in physical deviations by the autonomous vehicle. We evaluate REQUIEM's efficacy against both, standard PX4 controllers and the state-of-the-art SAVIOR anomaly detector. Across real-world quadrotor experiments and high-fidelity simulations (Gazebo/PX4), REQUIEM demonstrates significant deviations from planned mission paths while evading anomaly detection methods, without the need for intrusive root/administrative access. Our findings suggest that the very properties making state estimators reliable may constitute a security liability, motivating investigation into ``learnability'' as an attack surface in safety-critical systems.

cs.CR↗

arXiv2023

DeepMPR: Enhancing Opportunistic Routing in Wireless Networks through Multi-Agent Deep Reinforcement Learning

Opportunistic routing relies on the broadcast capability of wireless networks. It brings higher reliability and robustness in highly dynamic and/or severe environments such as mobile or vehicular ad-hoc networks (MANETs/VANETs). To reduce the cost of broadcast, multicast routing schemes use the connected dominating set (CDS) or multi-point relaying (MPR) set to decrease the network overhead and hence, their selection algorithms are critical. Common MPR selection algorithms are heuristic, rely on coordination between nodes, need high computational power for large networks, and are difficult to tune for network uncertainties. In this paper, we use multi-agent deep reinforcement learning to design a novel MPR multicast routing technique, DeepMPR, which is outperforming the OLSR MPR selection algorithm while it does not require MPR announcement messages from the neighbors. Our evaluation results demonstrate the performance gains of our trained DeepMPR multicast forwarding policy compared to other popular techniques.

cs.NI↗