Optimal Action

Optimal action selection, aiming to maximize cumulative rewards or minimize regret in dynamic environments, is a central problem across diverse fields like robotics, control systems, and economics. Current research focuses on developing algorithms that handle non-stationarity, uncertainty, and high-dimensional action spaces, employing techniques like model predictive control, reinforcement learning (with architectures such as transformers and neural networks), and optimal transport. These advancements are improving decision-making in complex systems, with applications ranging from autonomous driving and energy management to scientific discovery and multi-agent coordination.