👉 Probabilistic Dynamic Programming (PDP) is a mathematical framework used to solve complex decision-making problems in dynamic environments, particularly where uncertainty and time-varying factors are involved. It extends traditional Dynamic Programming (DP) by incorporating probabilistic models to handle stochastic processes and non-determinism. In PDP, decisions are made sequentially over time, with each decision point influenced by the current state and a probability distribution representing possible future states. The goal is to find an optimal policy that maximizes expected cumulative rewards over time, considering the inherent uncertainties. This approach is widely applied in fields such as economics, finance, operations research, and artificial intelligence for tasks like resource allocation, inventory management, and strategic planning under uncertainty.
