Reinforcement Learning for Gaming - Full Python Course

This course demonstrates how to train reinforcement learning agents for games using Python and Stable Baselines 3. Projects cover Mario, Doom, Street Fighter, and Chrome Dino, including environment setup, preprocessing, curriculum learning, reward shaping, hyperparameter tuning, and testing.

Syllabus

- START
- MARIO
- Mario Mission 1 - Setup Mario
- Mario Mission 2 - Preprocess Environment
- Mario Mission 3 - Build the RL Model
- Mario Mission 4 - Run the RL Model Live
- DOOM
- Doom Mission 1 - Get Vizdoom Working
- Doom Mission 2 - Setup OpenAI Gym Environment
- Doom Mission 3 - Train the RL Agent
- Doom Mission 4 - Test the RL Agent
- Doom Mission 5 - Training for Other Levels
- Doom Mission 6 - Curriculum Learning and Reward Shaping
- STREETFIGHTER
- Streetfighter Mission 1 - Setup Streetfighter
- Streetfighter Mission 2 - Preprocessing
- Streetfighter Mission 3 - Hyperparameter Tuning
- Streetfighter Mission 4 - Fine Tune the Model
- Streetfighter Mission 5 - Testing the Model
- DINO
- Dino Mission 1 - Install and Setup Dependencies
- Dino Mission 2 - Create a Custom OpenAI Gym Environment
- Dino Mission 3 - Train the RL Model
- Dino Mission 4 - Get the Model to Smash Chrome Dino
- Wrap Up