An environment for simulated parking lot tasks.
Project description
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.
Project details
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