Markov Decision Processes: Math of Decisions

Imagine a robust mathematical framework used to model sequential decision-making in environments where results are partly random.
In this video, we explore Markov Decision Processes (MDP). You will understand the real story behind this four-part structure and how it discovers an optimal policy, based on factual events.
In this video you will discover:
• How the system is defined by available states, possible actions, transition probabilities, and rewards.
• Why the primary objective is to choose actions that maximize long-term cumulative rewards.
• How computational algorithms like value iteration and policy iteration handle discrete and continuous time scales.
Beyond standard models, the framework can be expanded to include partial observability or specialized learning techniques like Q-learning.
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Видео Markov Decision Processes: Math of Decisions канала Clear Tech