Metadata-Version: 2.1
Name: bark-simulator
Version: 1.0.1
Summary: A tool for Behavior benchmARKing
Home-page: https://github.com/bark-simulator/bark
Author: Julian Bernhard, Klemens Esterle, Patrick Hart, Tobias Kessler
Author-email: autonomous-driving@fortiss.org
License: MIT
Description: <p align="center">
        <img src="https://github.com/bark-simulator/bark/raw/master/docs/source/bark_logo.jpg" alt="BARK" />
        </p>
        
        ![Ubtuntu-CI Build](https://github.com/bark-simulator/bark/workflows/CI/badge.svg)
        ![Ubtuntu-ManyLinux Build](https://github.com/bark-simulator/bark/workflows/ManyLinux/badge.svg)
        ![NIGHTLY LTL Build](https://github.com/bark-simulator/bark/workflows/NIGHTLY_LTL/badge.svg)
        ![CI RSS Build](https://github.com/bark-simulator/bark/workflows/CI_RSS/badge.svg)
        ![NIGHTLY Rules MCTS Build](https://github.com/bark-simulator/bark/workflows/NIGHTLY_RULES_MCTS/badge.svg)
        
        # BARK - a tool for **B**ehavior benchm**ARK**ing
        
        BARK is a semantic simulation framework for autonomous agents with a special focus on autonomous driving.
        Its behavior model-centric design allows for the rapid development, training and benchmarking of various decision-making algorithms.
        Due to its fast, semantic runtime, it is especially suited for computationally expensive tasks, such as reinforcement learning.
        
        ## BARK Ecosystem
        
        The BARK ecosystem is composed of multiple components that all share the common goal to develop and benchmark behavior models:
        
        * [BARK-ML](https://github.com/bark-simulator/bark-ml/): Machine learning library for decision-making in autonomous driving.
        * [BARK-MCTS](https://github.com/bark-simulator/planner-mcts): Integrates a template-based C++ Monte Carlo Tree Search Library into BARK to support development of both single- and multi-agent search methods.
        * [BARK-Rules-MCTS](https://github.com/bark-simulator/planner-rules-mcts): Integrates traffic rules within Monte Carlo Tree Search with lexicographic ordering.
        
        * [BARK-DB](https://github.com/bark-simulator/bark-databasse/): Provides a framework to integrate multiple BARK scenario sets into a database. The database module supports binary seriliazation of randomly generated scenarios to ensure exact  reproducibility of behavior benchmarks accross systems. 
        * [BARK-Rule-Monitoring](https://github.com/bark-simulator/rule-monitoring): Provides runtime verification of LTL Rules on simulated BARK traces.
        * [CARLA-Interface](https://github.com/bark-simulator/carla-interface): A two-way interface between [CARLA ](https://github.com/carla-simulator/carla) and BARK. BARK behavior models can control CARLA vehicles. CARLA controlled vehicles are mirrored to BARK.
        
        ## Quick Start
        ### Pip-package
        Bark is available as PIP-Package for Ubuntu and MacOS for Python>=3.7. You can install the latest version with 
        `pip install bark-simulator`. The Pip package supports full benchmarking functionality of existing behavior models and development of your models within python. The pip-package not yet includes MCTS and Carla interfaces. 
        
        After installing the package, you can have a look at the examples to check how to use BARK. 
        
        Highway: ' `import bark.examples.highway`:
        <p align="center">
        <img src="https://github.com/bark-simulator/bark/raw/master/docs/source/gifs/bark_highway.gif" alt="BARK" />
        </p>
        
        Merging: `import bark.examples.merging`:
        <p align="center">
        <img src="https://github.com/bark-simulator/bark/raw/master/docs/source/gifs/bark_merging.gif" alt="BARK" />
        </p>
        
        Intersection: `import bark.examples.intersection`:
        <p align="center">
        <img src="https://github.com/bark-simulator/bark/raw/master/docs/source/gifs/bark_intersection.gif" alt="BARK" />
        </p>
        
        ### Development setup
        If you want to write own behavior models in C++ or contribute to the development of Bark. Use `git clone https://github.com/bark-simulator/bark.git` or download the repository from this page.
        Then follow the instructions at [How to Install BARK](https://github.com/bark-simulator/bark/blob/master/docs/source/installation.md).
        
        To get step-by-step instructions on how to use BARK, you can run our IPython Notebook tutorials using `bazel run //docs/tutorials:run`.
        For a more detailed understanding of how BARK works, its concept and use cases have a look at our [documentation](https://bark-simulator.readthedocs.io/en/latest/about.html).
        
        ## Paper
        
        If you use BARK, please cite us using the following [paper](https://arxiv.org/abs/2003.02604):
        
        ```
        @inproceedings{Bernhard2020,
            title = {BARK: Open Behavior Benchmarking in Multi-Agent Environments},
            author = {Bernhard, Julian and Esterle, Klemens and Hart, Patrick and Kessler, Tobias},
            booktitle = {2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
            url = {https://arxiv.org/pdf/2003.02604.pdf},
            year = {2020}
        }
        ```
        
        ## License
        
        BARK specific code is distributed under MIT License.
        
Keywords: simulator autonomous driving machine learning
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3.7
Requires-Python: >=3.7
Description-Content-Type: text/markdown
