Linear Policy

Linear policies, a class of control strategies characterized by linear mappings from observations to actions, are a focus of current research in reinforcement learning and control due to their simplicity, interpretability, and amenability to theoretical analysis. Research emphasizes developing efficient algorithms for learning and optimizing these policies, particularly in complex, high-dimensional settings, often employing techniques like linear least-squares regression, evolution strategies, and gradient-based methods within various model architectures (e.g., Luenberger observers, sparse polynomial networks). This focus stems from the need for robust, efficient, and interpretable control solutions across diverse applications, ranging from robotics and population dynamics to optimal stopping and contextual pricing.