Metadata-Version: 2.1
Name: bark-simulator
Version: 0.0.5
Summary: A tool for Behavior benchmARKing
Home-page: https://github.com/bark-simulator/bark
Author: Fortiss
Author-email: cheema@fortiss.org
License: MIT
Description: <p align="center">
        <img src="docs/source/bark_logo.jpg" alt="BARK" />
        </p>
        
        ![CI Build](https://github.com/bark-simulator/bark/workflows/CI/badge.svg)
        ![NIGHTLY Build](https://github.com/bark-simulator/bark/workflows/NIGHTLY/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/): Develop behavior models based on machine learning library.
        * [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-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. 
        * [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.
        
        
        ## Paper
        If you use BARK, please cite us using the following paper:
        
        ```
        @misc{bernhard2020bark,
            title={BARK: Open Behavior Benchmarking in Multi-Agent Environments},
            author={Julian Bernhard and Klemens Esterle and Patrick Hart and Tobias Kessler},
            year={2020},
            eprint={2003.02604},
            archivePrefix={arXiv},
            primaryClass={cs.MA}
        }
        ```
        
        
        ## Quick Start
        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/pip_package_merged_dirs/docs/source/installation.md).
        
        After the installation, you can explore the examples by e.g. running `source dev_into.sh && bazel run //examples:od8_const_vel_two_agent`.
        
        <p align="center">
        <img src="docs/source/example_map.gif" alt="BARK" />
        </p>
        
        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).
        
        
        ## License
        BARK specific code is distributed under MIT License.
        
Keywords: simulator autonomous driving machine learning
Platform: UNKNOWN
Classifier: Development Status :: 1 - Planning
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: POSIX
Classifier: Programming Language :: Python :: 3.7
Requires-Python: >=3.7
Description-Content-Type: text/markdown
