parking-env 0.0.2

An environment for simulated parking lot tasks.

parking-env

parking-env is a gymnasium-based environment for reinforcement learning, written in a single Python file and accelerated by Numba. The environment is designed to simulate the task of parking a vehicle in a parking lot, where the agent controls the steering angle and the speed to park the vehicle successfully.

Installation

To install parking-env, you can clone the repository from GitHub:

git clone https://github.com/KexianShen/parking-env.git

Then, navigate to the root directory of the repository and run:

pip install -e .

This will install the environment and all its dependencies.

Usage

To use parking-env, you can import it in your Python code as follows:

import gymnasium as gym
import parking_env

env = gym.make("Parking-v0", render_mode="human")

Credits

parking-env is heavily inspired by the HighwayEnv environment, and some of its code was adapted for use in parking-env.

Additionally, parking-env uses the algorithms provided in CleanRL, a collection of clean implementations of popular RL algorithms.

License

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