Finite Horizon

Finite horizon problems in control and reinforcement learning focus on optimizing decisions over a fixed, limited time span, contrasting with infinite horizon approaches. Current research emphasizes efficient algorithms for solving these problems, particularly within model predictive control (MPC) and reinforcement learning frameworks, often employing techniques like policy gradients, Q-learning, and various neural network architectures to handle complex state and action spaces. This research is significant because finite horizon models are crucial for many real-world applications where time constraints are inherent, such as robotics, resource management, and financial trading, offering more realistic and practical solutions than infinite horizon counterparts.