Reinforcement Learning (RL) seeks to develop systems capable of autonomous decision-making by learning through interaction with their environment. Central to this process are reward engineering and reward shaping, which are essential for enhancing the efficiency and effectiveness of RL algorithms. These techniques guide agents toward desired behaviors, improve learning stability, and accelerate convergence by addressing challenges such as sparse and delayed rewards. However, the complexity of real-world environments and the computational demands of RL algorithms remain significant obstacles to broader adoption. Recent advancements in deep learning have enabled RL to handle high-dimensional state and action spaces, facilitating applications in robotics, autonomous driving, and complex decision-making tasks. In response to these developments, this paper provides one of the first comprehensive reviews of reward design in RL, with a focus on the methodologies and techniques underpinning reward engineering and shaping. By introducing a detailed taxonomy, critically analyzing current approaches, and highlighting their limitations, this work fills an important gap in the literature, offering insights into how reward structures can be optimized to meet the growing demands of modern AI systems.

This paper explores the integration of quantum computing, specifically quantum annealing, into robotics for inspecting electrical transmission lines. By using quantum annealing's computational power, we address the dynamics of robot inspectors on transmission line conductors, enhancing the computational efficiency of managing the movements of robot inspectors on transmission line conductors. The use of these robots is crucial for performing efficient and safe inspections. We discuss the mathematical foundations of quantum annealing, transforming differential equations governing robot dynamics into Ising model Hamiltonians suitable for quantum annealing devices. Through experiments using D-Wave's ‘Advantage’ quantum annealer, we demonstrate the viability of quantum annealing in solving complex robotics problems, paving the way for advanced applications in the field.

This work presents a sampling-based Quadratic Stability Control (QSC) framework for nonlinear mechanical systems with parametric uncertainty and compares it with a nominal Linear Quadratic Regulator (LQR). The proposed method synthesizes a single state-feedback gain by enforcing a common quadratic Lyapunov condition over a finite set of locally linearized models sampled in state and parameter space. A systematic numerical study evaluates the effects of the number of sampled models, state sampling radius, parameter uncertainty, and actuator configuration. Performance is assessed using trajectory mean-squared error, final position error, and offline controller-synthesis time. The results show that QSC has higher position error than nominal LQR at 5% uncertainty but lower error at 10% and 15% across five independently sampled designs, with greater force effort while maintaining comparable nominal performance. The common quadratic certificate covers the sampled models and their convex hull, not the full continuous nonlinear domain. These benefits are obtained at the cost of higher offline semidefinite-programming complexity.