bark-ml 0.3.1

Machine Learning Applied to Autonomous Driving

BARK-ML - Machine Learning for Autonomous Driving

Try it on Google Colab!

BARK-ML offers OpenAI-Gym environments for autonomous driving and reinforcement learning agents. BARK-ML is built upon a fast, semantic C++ back-end and fully wrapped to Python.

The BARK-ML package can be installed using pip install bark-ml.

Gym Environments

Highway Scenario

env = gym.make("highway-v0")

The highway scenario is a curved road with four lanes with all vehicles are controlled by the intelligent driver model (IDM). For more details have a look here.

Available environments:

• highway-v0: Continuous highway environment
• highway-v1: Discrete highway environment

The highway-v0 environment.

Merging Scenario

env = gym.make("merging-v0")

The ego agent is placed on the right lane and its goal on the left. All other vehicles are controlled by the MOBIL model. For more details have a look here.

Available environments:

• merging-v0: Continuous merging environment
• merging-v1: Discrete merging environment

The merging-v0 environment.

Unprotected Left Turn

env = gym.make("intersection-v0")

The ego vehicle starts on the bottom-right lane and its goal is set on the top-left lane (unprotected left turn). For more details have a look here.

Available environments:

• intersection-v0: Continuous intersection environment
• intersection-v1: Discrete intersection environment

The intersection-v0 environment.

Getting Started

A complete example using the OpenAi-Gym interface can be found here:

import gym
import numpy as np
# registers bark-ml environments
import bark_ml.environments.gym  # pylint: disable=unused-import

env = gym.make("merging-v0")

initial_state = env.reset()
done = False
while done is False:
  action = np.array([0., 0.]) # acceleration and steering-rate
  observed_state, reward, done, info = env.step(action)
  print(f"Observed state: {observed_state}, "
        f"Action: {action}, Reward: {reward}, Done: {done}")

Building From Source

Clone the repository using git clone https://github.com/bark-simulator/bark-ml, install the virtual python environment and activate it afterwards using:

bash utils/install.sh
source utils/dev_into.sh

Now - once in the virtual python environment - you can build any of the libraries or execute binaries within BARK-ML using Bazel. To run the getting started example from above, use the following command: bazel run //examples:continuous_env.

Documentation

Read the documentation online.

Publications

• Graph Neural Networks and Reinforcement Learning for Behavior Generation in Semantic Environments (IV 2020)
• BARK: Open Behavior Benchmarking in Multi-Agent Environments (IROS 2020)
• Counterfactual Policy Evaluation for Decision-Making in Autonomous Driving (IROS 2020, PLC Workshop)

License

BARK-ML code is distributed under MIT License.