Module 6 Markov Decision Process Explainer Video скачать в хорошем качестве

Module 6 Markov Decision Process Explainer Video

In this video explainer, we introduce Markov Decision Processes (MDPs) — a foundational framework for modeling sequential decision-making under uncertainty. MDPs form the backbone of many modern AI systems, including robotics, reinforcement learning, autonomous navigation, and intelligent control systems. You will learn how agents reason about long-term rewards, how optimal decisions are computed, and how uncertainty is handled mathematically using transition probabilities and reward functions. 📘 Topics Covered in This Lecture: What is a Markov Decision Process (MDP)? The Markov property and state transitions States, actions, rewards, and transition models Policies and value functions Value Iteration and Policy Iteration Discount factors and long-term planning Applications of MDPs in AI and robotics 🎯 Learning Objectives: By the end of this lecture, you will be able to: Explain the components of an MDP Model real-world decision problems as MDPs Understand how optimal policies are computed Compare value iteration and policy iteration Explain how MDPs relate to reinforcement learning #ArtificialIntelligence #MarkovDecisionProcess #MDP #ReinforcementLearning #AIAlgorithms #DecisionMaking #CS569 #MachineLearning