Introduction to Reinforcement Learning

Yes. This is a free online self-paced course designed for beginners.

No. This course focuses on basic concepts and does not require prior coding knowledge.

You will learn the basics of reinforcement learning, including agents, environments, rewards, policies, and decision-making.

Students, beginners, and professionals from any background interested in AI can join.

Yes. Learners receive an e-Certification after completing the course.

Reinforcement learning is a type of machine learning where an agent learns by interacting with an environment and receiving rewards or penalties based on its actions.

Yes. Supervised learning uses labeled examples, while reinforcement learning focuses on learning through actions, feedback, rewards, and trial-and-error interaction.

The Introduction to Reinforcement Learning course is designed as a 2–3 week online self-paced course.

Yes. This course gives learners a simple foundation in decision-making systems, agent-based learning, and reward-based learning before moving into advanced AI, robotics, and machine learning topics.

The course explains agents, environments, rewards, actions, policies, and decision-making using simple examples, without requiring prior programming, advanced mathematics, or machine learning knowledge. The Introduction to Reinforcement Learning course provides a simple and structured introduction to how machines learn through interaction and feedback. It helps learners understand decision-making systems and prepares them for advanced topics in artificial intelligence, robotics, and machine learning.