This repository contains a reinforcement learning-based solution where an agent learns to navigate a racetrack. The goal of the project is to develop a model that allows a car to autonomously learn how to drive along a track and improve its lap times through trial and error using reinforcement learning techniques.
- Reinforcement Learning Algorithm: Utilizes Q-learning or Deep Q-Networks (DQN) to enable the car to make decisions and learn the best actions for optimal lap times.
- Environment Simulation: The racetrack is simulated using Python, and the car's actions, such as steering and acceleration, are optimized based on feedback from the environment.
- Model Training: The agent is trained in an iterative process, improving its strategy with each lap, gradually reducing its lap time as it discovers the most efficient route.
- Performance Metrics: The model's performance is evaluated by measuring lap time improvement and the agent's ability to avoid obstacles and maintain the optimal racing line.
- Reinforcement Learning: Q-learning, Deep Q-Networks (DQN)
- Python: For implementation and simulation