What will you learn in this Fundamentals of Reinforcement Learning Course
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Markov Decision Processes (MDPs): Formalize decision-making problems using MDPs, a foundational framework in reinforcement learning.
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Exploration vs. Exploitation: Understand strategies to balance exploring new actions and exploiting known rewards.
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Value Functions: Learn about value functions as tools for optimal decision-making.
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Dynamic Programming: Implement dynamic programming methods for solving MDPs efficiently.
Program Overview
1. Welcome to the Course!
⏳ 50 minutes
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Introduction to the course structure and objectives.
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Meet your instructors and understand the roadmap for the specialization.
2. An Introduction to Sequential Decision-Making
⏳ 3 hours
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Explore the exploration-exploitation trade-off in decision-making.
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Implement incremental algorithms for estimating action-values.
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Compare different algorithms for exploration.
3. Markov Decision Processes
⏳ 3 hours
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Translate real-world problems into the MDP framework.
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Understand goal-directed behavior through reward maximization.
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Differentiate between episodic and continuing tasks.
4. Value Functions & Bellman Equations
⏳ 3 hours
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Define policies and value functions.
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Understand Bellman equations and their role in reinforcement learning
5. Dynamic Programming
⏳ 3 hours
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Compute value functions and optimal policies using dynamic programming.
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Implement policy evaluation and improvement methods.
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Understand Generalized Policy Iteration as a template for constructing algorithms.
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Job Outlook
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Equips learners for roles such as Machine Learning Engineer, AI Researcher, and Data Scientist.
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Provides foundational knowledge applicable in industries like robotics, finance, healthcare, and game development.
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Enhances understanding of decision-making systems and intelligent agent design.
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Strengthen your understanding of reinforcement learning and AI-driven decision-making with these curated courses designed to provide both theoretical foundations and practical applications.
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What Is Data Management – Learn how proper data organization and management are critical for training reinforcement learning models effectively.
