Deep RL Racecar

Navigating the Future: My Autonomous Racecar Project 🏁

Welcome to my exciting journey into the world of autonomous racecar navigation! I'm on a mission to train a racecar to conquer complex racetracks autonomously, using cutting-edge techniques in deep reinforcement learning. Join me as I guide this machine to stay on the midline while respecting track boundaries, all while maximizing rewards.

Unleashing the Power of Reinforcement Learning

I've taken the helm in this project, employing state-of-the-art deep reinforcement learning techniques to transform this ambitious goal into reality. My weapon of choice? Proximal Policy Optimization (PPO), a proven algorithm that excels at training agents for complex tasks.

Rewriting the Rules with Reward Maximization

Training an autonomous racecar isn't just about steering and accelerating; it's about creating an experience worth striving for. That's why I've meticulously crafted reward functions that motivate the racecar to exhibit the behavior we desire:

Code Variations for Exploration

In my quest for the ultimate racing agent, I've explored multiple variations of the reward functions. Check out these code variants:

Autonomous Race Car

Training Track Blueprint