# Reinforcement Learning
# Learning Objectives
These topics will be covered in this module’s knowledge check and entry ticket.
- Define key concepts in reinforcement learning, including agents, environments, states, actions, and rewards.
- Understand the exploration vs. exploitation trade-off and its importance in reinforcement learning algorithms.
- Identify real-world applications of reinforcement learning.
- Discuss the challenges and limitations of reinforcement learning.
# Reinforcement Learning Lessons
# M9 Assignment
Visual Studio Code: Visual Studio Code - Code Editing. Redefined Python: Python Release Python 3.11.9 | Python.org
- Check “Add Python to PATH” when installing
CartPole Documentation: Cart Pole - Gymnasium Documentation (farama.org)
# Instructions
- Download and install VS Code and Python (links above)
- Download the CartPoleAssignment.ipynb (github.com) and open it in VS Code
- Install the Jupyter extension (see video)
- Read the text and understand each code cell as you run them
- Fill in and successfully execute and code cells that require your input
- Upload your completed CartPoleAssignment.ipynb file to the assignment submission on Canvas.
# Optional Resources
- If you’re interested in learning more about how to implement RL -> Reinforcement Learning in 3 Hours | Full Course using Python (youtube.com)