A tool for Behavior benchmARKing
Project description
BARK - a tool for Behavior benchmARKing
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: Develop behavior models based on machine learning library.
-
BARK-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: 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: A two-way interface between 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.
After the installation, you can explore the examples by e.g. running source dev_into.sh && bazel run //examples:od8_const_vel_two_agent
.
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.
License
BARK specific code is distributed under MIT License.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Hashes for bark_simulator-0.0.8-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | c89043ec518b45ee7d746ba0055309ce69bd551889d5c1a8418ac7e145121ee1 |
|
MD5 | fa0a422a8c2a9a66d1e954ee441bd600 |
|
BLAKE2b-256 | 7e1bcf6bf468450bcf4374e6853b56e2050b0c77f05898ab1499102f862ce5a5 |