Computer vision and self-driving cars are trending topics in machine learning and artificial intelligence. If you’re new to this field and want to explore AI applications, then this guide will help you create a neural network driver for TrackMania, a thrilling racing game that can serve as a perfect testing ground for AI algorithms!

Intro and Objectives

The primary goal of this project is to develop a competitive AI driver in the exhilarating world of TrackMania. By using reinforcement learning and supervised learning techniques, we aim to enhance our AI’s performance and possibly advance our findings to real-world car applications.

Setting Up Your Environment

Follow these steps to get your project up and running:

• Download Trackmania and Python, ensuring that you install all necessary packages.
• Download and install Openplanet, a powerful tool for integration with TrackMania.
• Clone the repository into your local environment.
• Move the TMRL_GrabDataExt.op file from the Scripts directory into C:\Users\{username}\OpenplanetNext\Plugins.
• Launch Trackmania. Press F3 to open Openplanet and confirm the script is running.
• Optionally, switch to windowed mode for easier management during gameplay.
• Tweak the RL_Driver.py and RL_Trainer.py files to select maps and algorithms.
• Run python RL_Driver.py. Ensure you are using the map file of the track you want to drive on.

Understanding the Code: A Simple Analogy

Imagine you are teaching a child how to ride a bike, but instead of giving them all the instructions at once, you focus on a few elements at a time. This is akin to what happens in our TrackmaniaEnv.py file. Here’s a breakdown of our components:

• **Previous Actions**: Like teaching the child to steer or brake in incremental steps, our code tracks past actions (steer, throttle, brake) to inform future decisions.
• **Vehicle Velocity**: Just as a rider learns to balance speed while turning, the AI monitors its current speed.
• **Wall Contact**: Similar to observing obstacles while biking, the AI detects contacts with walls.
• **Wall Distances**: Like looking around to gauge distance from barriers, the AI considers distances to walls for smarter navigation.

This systematic awareness allows your AI to adapt and improve its performance on the track over time!

Reward Mechanism & Learning Process

The project also implements a reward mechanism based on reinforcement learning principles. The AI is rewarded for achieving successful driving patterns, adapting its strategy as it learns – similar to how a cyclist receives praise for successfully navigating tricky terrain.

Troubleshooting Common Issues

While working on your AI driver, you may encounter some common issues:

• Openplanet Not Loading: If Openplanet fails to load, consider running Trackmania directly from the .exe file or use Ubisoft Connect.
• Script Not Showing as Active: Make sure to verify the Openplanet log after pressing F3. If the script isn’t running, attempt to reload the plugin.
• Input Errors: Ensure that you have the appropriate dataset and the maps are processed correctly before running your training scripts.
• For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Current Progress and Results

While this project is still a work in progress, I am thrilled to share some preliminary results from our various models, including SAC and PPO. These demonstrate a competitive edge in driving performance, paving the way for further refinement.

Final Thoughts

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.

With this guide, you are well on your way to building a formidable AI driver for TrackMania. Experiment, learn, and let your creativity skyrocket! Enjoy the journey!