F1_Reinforcement_Learning_Q_Learning

This is a Custom Gym Environment named: YashrajEnv

Overview

YashrajEnv is a custom Gym environment designed for an F1-Racing game simulation. The environment provides a grid-based track where an agent navigates to reach a goal while managing fuel and encountering various obstacles. Currently controlled manually, will add Q-Learning(Reinforcement Learning method in the upcoming days).

Features

• Grid Size: Default grid size is 10x10.
• Initial Fuel: The agent starts with a specified amount of fuel.
• Actions: Four discrete actions available (up, down, right, left).
• Observation Space: 2D position on the grid.
• Rewards: The agent receives rewards based on its actions and proximity to the goal or obstacles.
• Agent: It includes Lewis Hamilton driving the Mercedes W11 F-1 car.
• Obstacles: Includes red flags, yellow flags, and other vehicle, each affecting the agent's reward or behavior.
• Visualization: Renders the environment using Pygame for visual feedback.

Installation

1. Clone the repository: git clone https://github.com/Yashraj19032001/F1_Reinforcement_Learning_Q_Learning.git cd your_repository

2. Install dependencies: pip install -r requirements.txt

Usage

Creating the Environment:

import gymnasium as gym

Create an instance of the environment

env = gym.make('YashrajEnv', grid_size=10, initial_fuel=17)

Environment Configuration:

• Adding Obstacles: Red flags, yellow flags, and red bull states can be added using specific methods (add_red_flag_states, add_yellow_flag_states, add_red_bull_states).

Example: env.add_red_flag_states(red_flag_state_coordinates=(4, 1))

• Setting Track States: Define track coordinates to customize the environment layout.

Example: track_state_coordinates_list = [(0, 5), (0, 6), ...] env.add_track_states(track_state_coordinates_list)

Running the Environment:

observation, info = env.reset() done = False

while not done: action = int(input("Choose action (0=Up, 1=Down, 2=Right, 3=Left): ")) observation, reward, done, info = env.step(action) env.render()

Rendering:

The environment renders using Pygame, providing visual feedback on the agent's position, obstacles, and goal.

Contributing

Contributions are welcome! Feel free to fork the repository and submit pull requests.

License

This project is licensed under the MIT License - see the LICENSE file for details.