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align=right width=250px  src=\"pgdrive/assets/PGDrive.png\" /\u003e\n\n# PGDrive: an open-ended driving simulator with infinite scenes\n\n[![build](https://github.com/decisionforce/pgdrive/workflows/test/badge.svg)](http://github.com/decisionforce/pgdrive/actions)\n[![codecov](https://codecov.io/gh/decisionforce/pgdrive/branch/main/graph/badge.svg?token=1ZYN8L5397)](https://codecov.io/gh/decisionforce/pgdrive)\n[![Documentation](https://readthedocs.org/projects/pgdrive/badge/?version=latest)](https://pgdrive.readthedocs.io)\n[![GitHub license](https://img.shields.io/github/license/decisionforce/pgdrive)](https://github.com/decisionforce/pgdrive/blob/main/LICENSE.txt)\n[![GitHub stars](https://img.shields.io/github/stars/decisionforce/pgdrive)](https://github.com/decisionforce/pgdrive/stargazers)\n\n**This project is deprecated and merged into [MetaDrive](https://github.com/decisionforce/metadrive). Please follow the MetaDrive repo for the latest development and maintenance.** \n\n**[  📺 [Website](https://decisionforce.github.io/pgdrive/) | 🏗 [Github Repo](https://github.com/decisionforce/pgdrive) | 📜 [Documentation](https://pgdrive.readthedocs.io/) | 🎓 [Paper](https://arxiv.org/pdf/2012.13681) ]**\n\nWelcome to PGDrive! PGDrive is an driving simulator with many key features, including:\n\n- 🎏 **Lightweight**: Extremely easy to download, install and run in almost all platforms.\n- 📷 **Realistic**: Accurate physics simulation and multiple sensory inputs.\n- 🚀 **Efficient**: Up to 500 simulation step per second and easy to parallel.\n- 🗺 **Open-ended**: Support generating infinite scenes and configuring various traffic, vehicle, and environmental settings.\n\n\n\u003cimg src=\"docs/images/panel.jpg\"\u003e\n\n\n\n## 🛠 Quick Start\nPlease install PGDrive via:\n\n```bash\npip install pgdrive\n```\n\nIf you wish to contribute to this project or make some modification, you can clone the latest version of PGDrive locally and install via:\n\n```bash\ngit clone https://github.com/decisionforce/pgdrive.git\ncd pgdrive\npip install -e .\n```\n\nYou can verify the installation and efficiency of PGDrive via running:\n\n```bash\npython -m pgdrive.examples.profile_pgdrive\n```\n\nThe above script is supposed to be runnable in all places.\nNote that please do not run the above command in the folder that has a sub-folder called `./pgdrive`.\n\n## 🚕 Examples\n\nPlease run the following line to drive the car in the environment manually with keyboard!\n\n```bash\npython -m pgdrive.examples.enjoy_manual\n```\n\nYou can also enjoy a journey carrying out by our professional driver pretrained from reinforcement learning! \n\n```bash\npython -m pgdrive.examples.enjoy_expert\n```\n\nA fusion of expert and manual controller, \nwhere the expect will try to rescue the manually controlled vehicle from danger, \ncan be experienced via:\n\n```bash\npython -m pgdrive.examples.enjoy_saver\n```\n\nTo show the main feature, procedural generation, we provide a script to show BIG:\n\n```bash\npython -m pgdrive.examples.render_big\n```\n\n*Note that the above three scripts can not be run in headless machine.* \nPlease refer to the installation guideline in documentation for more information.\n\nRunning the following line allows you to draw the generated maps:\n\n```bash\npython -m pgdrive.examples.draw_maps\n```\n\nTo build the environment in python script, you can simply run:\n\n```python\nimport pgdrive  # Import this package to register the environment!\nimport gym\n\nenv = gym.make(\"PGDrive-v0\", config=dict(use_render=True))\n# env = pgdrive.PGDriveEnv(config=dict(environment_num=100))  # Or build environment from class\nenv.reset()\nfor i in range(1000):\n    obs, reward, done, info = env.step(env.action_space.sample())  # Use random policy\n    env.render()\n    if done:\n        env.reset()\nenv.close()\n```\n\nWe also prepare a Colab which demonstrates some basic usage of PGDrive as follows:\n[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/decisionforce/PGDrive/blob/main/pgdrive/examples/Basic%20PGDrive%20Usages.ipynb)\n\n## 📦 Predefined environment sets\n\nWe also define several Gym environment names, so user can start training in the minimalist manner:\n\n```python\nimport gym\nimport pgdrive  # Register the environment\nenv = gym.make(\"PGDrive-v0\")\n```\n\nThe following table presents some predefined environment names. \n\n|\u0026nbsp;  Gym Environment Name   | Random Seed Range | Number of Maps | Comments                                          |\n| ----------------------- | ----------------- | -------------- | ------------------------------------------------------- |\n| `PGDrive-test-v0`       | [0, 200)          | 200            | Test set, not change for all experiments.               |\n| `PGDrive-validation-v0` \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;|[200, 1000)|800| Validation set.|\n| `PGDrive-v0`            | [1000, 1100)      | 100            | Default training setting, for quick start.              |\n| `PGDrive-10envs-v0`     | [1000, 1100)      | 10             | Training environment with 10 maps.                      |\n| `PGDrive-1000envs-v0`   | [1000, 1100)      | 1000           | Training environment with 1000 maps.                    |\n| `PGDrive-training0-v0`  | [3000, 4000)      | 1000           | First set of 1000 environments.                         |\n| `PGDrive-training1-v0`  | [5000, 6000)      | 1000           | Second set of 1000 environments.                        |\n| `PGDrive-training2-v0`  | [7000, 8000)      | 1000           | Thirds set of 1000 environments.                        |\n| ...                     |                   |                | *More map set can be added in response to the requests* |\n\n## 🏫 Documentations\n\nMore information about PGDrive can be found in [PGDrive Documentation](https://pgdrive.readthedocs.io). \nBesides, the training code of our [paper](https://arxiv.org/pdf/2012.13681) can be found in [this repo](https://github.com/decisionforce/pgdrive-generalization-paper).\n\n## 📎 Citation\n\n\nIf you find this work useful in your project, please consider to cite it through:\n\n```\n@article{li2020improving,\n  title={Improving the Generalization of End-to-End Driving through Procedural Generation},\n  author={Li, Quanyi and Peng, Zhenghao and Zhang, Qihang and Qiu, Cong and Liu, Chunxiao and Zhou, Bolei},\n  journal={arXiv preprint arXiv:2012.13681},\n  year={2020}\n}\n```\n\n[![Codacy Badge](https://app.codacy.com/project/badge/Grade/2d6fabe328a644b49e1269497b741057)](https://www.codacy.com/gh/decisionforce/pgdrive/dashboard?utm_source=github.com\u0026amp;utm_medium=referral\u0026amp;utm_content=decisionforce/pgdrive\u0026amp;utm_campaign=Badge_Grade)\n[![GitHub contributors](https://img.shields.io/github/contributors/decisionforce/pgdrive)](https://github.com/decisionforce/pgdrive/graphs/contributors)\n[![GitHub forks](https://img.shields.io/github/forks/decisionforce/pgdrive)](https://github.com/decisionforce/pgdrive/network)\n[![GitHub issues](https://img.shields.io/github/issues/decisionforce/pgdrive)](https://github.com/decisionforce/pgdrive/issues)\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdecisionforce%2Fpgdrive","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdecisionforce%2Fpgdrive","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdecisionforce%2Fpgdrive/lists"}