ECE 414 / 517 · Course MaterialsGoogle Colab · Python
Reinforcement Learning
Coding Tutorials
Practical notebooks for building RL environments, estimating value functions, and learning control policies. Choose a module and open its notebook in Google Colab.
No local installation required.
The notebooks can be opened directly in Colab. Use File → Save a copy in Drive before editing so your work is saved to your own account.
The notebooks can be opened directly in Colab. Use File → Save a copy in Drive before editing so your work is saved to your own account.
Interactive Tutorials
Each module has an exercise notebook and a separate reference solution. The solution passcode will be provided in class.
| Module | Topics | Exercise | Solution |
|---|---|---|---|
| Basics of RL IIntroductory methods | Gymnasium API, tabular RL, prediction and control | Open in Colab | |
| Basics of RL IIAdvanced methods | Policy gradient algorithms | Open in Colab |
Instructions for Students
- Open a notebook. Choose a module and select Open in Colab.
- Save your own copy. In Colab, select File → Save a copy in Drive.
- Work through the cells. Run each section in order, complete the TODOs, and review training results and visualizations.
Course Repository
The exercise and solution notebooks are stored in the top-level
RL-basics and policy-gradient folders.
When opened in Google Colab, the notebooks automatically load their
supporting files from tutorial-utk-main in the same course repository.
# Course repository https://github.com/utkauraslab/ECE414-517_RL # Support-file locations used by the notebooks tutorial-utk-main/RL-basics tutorial-utk-main/RL-gradient