bark-ml 0.2.2

Machine Learning Applied to Autonomous Driving

BARK-ML - Machine Learning for Autonomous Driving

BARK-ML provides simple-to-use OpenAi-Gym environments for several scenarios, such as highway driving, merging and intersections. Additionally, BARK-ML integrates state-of-the-art machine learning libraries to learn driving behaviors for autonomous vehicles.

BARK-ML supported machine learning libraries:

• TF-Agents
• Baselines (Planned)
• Diadem

Install BARK-ML using pip install bark-ml.

Gym Environments

import gym
import numpy as np
import bark_ml.environments.gym

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

env.reset()
done = False
while done is False:
  # random action
  action = np.random.uniform(
    low=np.array([-0.5, -0.1]), high=np.array([0.5, 0.1]), size=(2, ))
  # step the world
  observed_next_state, reward, done, info = env.step(action)
  print(f"Observed state: {observed_next_state}, Action: {action}, Reward: {reward}, Done: {done}")

Available environments:

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

TF-Agents

SAC-Agent learning a merging scenario:

import gym
import os
os.environ['GLOG_minloglevel'] = '3' 

# BARK imports
from bark.runtime.commons.parameters import ParameterServer
from bark.runtime.viewer.matplotlib_viewer import MPViewer

# BARK-ML imports
from bark_ml.environments.blueprints import ContinuousMergingBlueprint
from bark_ml.environments.single_agent_runtime import SingleAgentRuntime
from bark_ml.library_wrappers.lib_tf_agents.agents import BehaviorSACAgent
from bark_ml.library_wrappers.lib_tf_agents.runners import SACRunner


params = ParameterServer()
params["World"]["remove_agents_out_of_map"] = True

# create environment
bp = ContinuousMergingBlueprint(params,
                                number_of_senarios=2500,
                                random_seed=0)
env = SingleAgentRuntime(blueprint=bp,
                         render=False)

# SAC-agent
sac_agent = BehaviorSACAgent(environment=env, params=params)
env.ml_behavior = sac_agent
runner = SACRunner(params=params,
                   environment=env,
                   agent=sac_agent)
runner.Train()
runner.Visualize(5)

License

BARK-ML specific code is distributed under MIT License.