arXiv subjects

Klinsmann Agyei

Publications and source records attributed to Klinsmann Agyei.

3 recordsLinked to original sources

arXiv2026

Generalist AI Control: Towards Multi-purpose Adaptive Algorithms

Traditional controllers are designed for specific systems and do not transfer across different system orders and dynamics. We present a Generalist Controller, a learning-based controller capable of controlling systems of varying orders and dynamics. The approach introduces a novel dynamic state-space representation using attention mechanisms with masking, enabling a single neural network, trained in one shot, to handle systems with different dimensions without architectural modifications by assigning a system tag to each system. We generated 314,630 demonstrations from 25 diverse systems, including stable, unstable, minimum-phase, and non-minimum-phase dynamics, spanning linear and nonlinear systems from autonomous underwater and aerospace vehicles to mechanical systems and chemical processes. The model learns cross-system control strategies through multi-scale temporal processing and a mixture-of-experts architecture. Simulation results demonstrate that the proposed generalist controller achieves comparable performance to system-specific LQI controllers across all tested systems, including challenging cases such as non-minimum-phase and unstable dynamics, whilst generalising to unseen operating conditions including actuator saturation, noise, disturbance, and reference trajectories not encountered during training. This work represents a significant step towards generalist control policies within a defined family of dynamical systems, demonstrating effective control across a range of single-input single-output (SISO) systems of varying order and dynamics using a single learned policy without system-specific tuning.

cs.AI↗

arXiv2025

Deep Reinforcement Learning in Applied Control: Challenges, Analysis, and Insights

Over the past decade, remarkable progress has been made in adopting deep neural networks to enhance the performance of conventional reinforcement learning. A notable milestone was the development of Deep Q-Networks (DQN), which achieved human-level performance across a range of Atari games, demonstrating the potential of deep learning to stabilise and scale reinforcement learning. Subsequently, extensions to continuous control algorithms paved the way for a new paradigm in control, one that has attracted broader attention than any classical control approach in recent literature. These developments also demonstrated strong potential for advancing data-driven, model-free algorithms for control and for achieving higher levels of autonomy. However, the application of these methods has remained largely confined to simulated and gaming environments, with ongoing efforts to extend them to real-world applications. Before such deployment can be realised, a solid and quantitative understanding of their performance on applied control problems is necessary. This paper conducts a comparative analysis of these approaches on four diverse benchmark problems with implementation results. This analysis offers a scrutinising and systematic evaluation to shed light on the real-world capabilities and limitations of deep reinforcement learning methods in applied control settings.

eess.SY↗

arXiv2024

Large Language Model-based Decision-making for COLREGs and the Control of Autonomous Surface Vehicles

In the field of autonomous surface vehicles (ASVs), devising decision-making and obstacle avoidance solutions that address maritime COLREGs (Collision Regulations), primarily defined for human operators, has long been a pressing challenge. Recent advancements in explainable Artificial Intelligence (AI) and machine learning have shown promise in enabling human-like decision-making. Notably, significant developments have occurred in the application of Large Language Models (LLMs) to the decision-making of complex systems, such as self-driving cars. The textual and somewhat ambiguous nature of COLREGs (from an algorithmic perspective), however, poses challenges that align well with the capabilities of LLMs, suggesting that LLMs may become increasingly suitable for this application soon. This paper presents and demonstrates the first application of LLM-based decision-making and control for ASVs. The proposed method establishes a high-level decision-maker that uses online collision risk indices and key measurements to make decisions for safe manoeuvres. A tailored design and runtime structure is developed to support training and real-time action generation on a realistic ASV model. Local planning and control algorithms are integrated to execute the commands for waypoint following and collision avoidance at a lower level. To the authors' knowledge, this study represents the first attempt to apply explainable AI to the dynamic control problem of maritime systems recognising the COLREGs rules, opening new avenues for research in this challenging area. Results obtained across multiple test scenarios demonstrate the system's ability to maintain online COLREGs compliance, accurate waypoint tracking, and feasible control, while providing human-interpretable reasoning for each decision.

cs.RO↗