What is practical-reinforcement-learning---coursera-advanced-machine-learning?

This repository is a collection of completed assignments from the Practical Reinforcement Learning course, which is part of the Advanced Machine Learning specialization on Coursera. If you are working through that course and want to compare your solutions or need help getting unstuck, these notebooks give you a working reference. Reinforcement learning is a branch of machine learning where a computer agent learns to make good decisions through trial and error. The agent interacts with an environment, receives rewards or penalties, and gradually figures out strategies that maximize its long-term success. The assignments here walk through building such agents step by step, covering foundational algorithms and techniques used in the field. The content is aimed at people studying reinforcement learning who want practical, hands-on experience rather than pure theory. A typical scenario might involve training an agent to balance a pole, navigate a grid, or play a simple game. Instead of reading about how these algorithms work, you open a notebook, run the code, and see the agent improve over time. The repository consists entirely of Jupyter notebooks, which mix explanatory text with runnable code. That format makes it easy to read along and experiment without setting up a separate project from scratch. The README itself does not go into detail about what each notebook covers, so you would need to browse the files directly to see which topics are included. If you are enrolled in the Coursera specialization, this collection can serve as a helpful companion as you work through the same material.