This beginner-friendly course on reinforcement learning equips you with the foundational and practical knowledge needed to understand and apply key RL concepts in real-world scenarios. Start by exploring what reinforcement learning is, why it matters, and how it differs from supervised and unsupervised learning. Learn essential terms and core principles through relatable examples. Dive deeper into the mechanics of decision-making with the Markov Decision Process (MDP), the backbone of RL. Gain practical experience by observing step-by-step demos that show how agents interact with environments to learn optimal behaviors.
Fundamental of Reinforcement Training
Gain insight into a topic and learn the fundamentals.
Beginner level
Recommended experience
3 hours to complete
Flexible schedule
Learn at your own pace
What you'll learn
Understand the fundamentals of reinforcement learning and its real-world applications
Distinguish reinforcement learning from supervised and unsupervised learning
Learn core concepts like the Markov Decision Process (MDP) for decision-making
Observe how agents learn through environment interaction using step-by-step demos
Skills you'll gain
Details to know
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Recently updated!
July 2025
Assessments
6 assignments
Taught in English
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There are 2 modules in this course
Explore the foundations of reinforcement learning in this beginner-friendly course. Understand what reinforcement learning is, why it matters, and how it differs from supervised and unsupervised learning. Learn key concepts and important terms through relatable examples that demonstrate real-world applications. Ideal for learners aiming to build a strong base in AI, machine learning, and decision-making systems.
What's included
6 videos1 reading3 assignments
Explore core reinforcement learning concepts in this hands-on course. Understand the Markov Decision Process (MDP) and how it forms the backbone of decision-making in RL. Watch reinforcement learning in action through step-by-step demos that show how agents learn from environments. Ideal for learners looking to gain practical insights into how reinforcement learning works in real-world scenarios.
What's included
4 videos3 assignments
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Frequently asked questions
Reinforcement in training refers to the process of encouraging desired behaviors through rewards or penalties, guiding the learning process.
The main purpose is to enable an agent to learn optimal behavior by interacting with an environment and receiving feedback in the form of rewards.
The four key components are: the agent, the environment, actions, and rewards.
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