tmlearning 1.2.0

TMLearning

TMLearning is a Python library for training and deploying custom TrackMania AIs using machine learning. It works with any TrackMania version—or any game that uses only arrow keys—by training a convolutional neural network (CNN) to imitate your driving style from screenshots and predict the next key presses.

Note: To capture keyboard input when TrackMania is not in focus, you must run the application running TMLearning with administrator privileges.

Features

Installation

pip install tmlearning

Quickstart

1. Input Testing

   from tmlearning import wasd_key_test, arrow_key_test
   
   # Test WASD input
   wasd_key_test()
   
   # Test arrow-key input
   arrow_key_test()
   

   Press your chosen keys to confirm they’re detected correctly.

2. Initialize the Bot

   from tmlearning import GeneralTMLearning
   bot = GeneralTMLearning(
       name="my_bot",
       keys="ARROW",                 # or "WASD"
       data_capture_interval=0.1,
       exec_capture_interval=0.1,
       save_frequency=None,
       img_size=(160, 120),
       cnn_test_percentage=0.2,
       cnn_epochs=10,
       cnn_batch_size=32,
       verbose=True
   )
   

3. Create a Dataset

   bot.create_database()
   

   • Press Enter to start.
   • After a 5‑second countdown, drive in TrackMania.
   • Hold your stop key (default z) for ~2× data_capture_interval seconds to stop and save.

4. Train the Model

   bot.train_model()
   

5. Run the Model

   bot.run_model()
   

   • Press Enter, switch to TrackMania within 5 seconds, and let it drive for you.
   • Use Ctrl+C to stop.

File Structure

When you instantiate GeneralTMLearning(name="my_bot"), a folder named my_bot_bot/ is created containing:

Configuration

• On first initialization, my_bot_config.pkl records all parameters.
• Re-initializing with the same name loads existing settings.
• Changing any attribute (e.g. bot.img_size = (200,150)) automatically updates the config file.

File Management

CNN Architecture (v1.2.0)

Currently fixed; customization coming in v1.3.

Version History

• 1.2.0

  • Switched to CNN architecture.
  • Added file‑deletion methods (delete_all_files, delete_config_file, delete_dataset_file).
  • Renamed main class to GeneralTMLearning in preparation for TMNFLearning.

• 1.1.0

  • Initial release.