kuds/rl-car-racing
Repository containing code and notebooks exploring how to solve Gymnasium's Car Racing through Reinforcement Learning
This project explores different reinforcement learning approaches to train an AI agent to drive a car in the Car Racing game environment. It takes in game observations (like screen images) and outputs optimal driving actions (steering, acceleration, braking) to achieve high scores. This is for AI researchers or students interested in comparing reinforcement learning algorithms on a classic control problem.
Use this if you are an AI researcher or student wanting to understand and compare the performance of various reinforcement learning algorithms (SAC, DQN, PPO) on a continuous control task like car racing.
Not ideal if you are looking for a plug-and-play solution for real-world autonomous driving or a high-performance gaming AI without understanding the underlying reinforcement learning principles.
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Language
Jupyter Notebook
License
MIT
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Last pushed
Feb 08, 2026
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