metadrive-simulator 0.2.3

An open-ended driving simulator with infinite scenes

MetaDrive: Composing Diverse Driving Scenarios for Generalizable RL

[ Website | Documentation | Demo Video (TBD) ]

MetaDrive is a driving simulator with the following key features:

• Compositional: It supports generating infinite scenes with various road maps and traffic settings for the research of generalizable RL.
• Lightweight: It is easy to install and run. It can run up to 300 FPS on a standard PC.
• Realistic: Accurate physics simulation and multiple sensory input including Lidar, RGB images, top-down semantic map and first-person view images.

🛠 Quick Start

Install MetaDrive via:

git clone https://github.com/decisionforce/metadrive.git
cd metadrive
pip install numpy cython
pip install -e .

or

pip install metadrive-simulator

You can verify the installation of MetaDrive via running the testing script:

# Go to a folder where no sub-folder calls metadrive
python -m metadrive.examples.profile_metadrive

Note that please do not run the above command in a folder that has a sub-folder called ./metadrive.

🚕 Examples

Run the following command to launch a simple driving scenario with auto-drive mode on. Press W, A, S, D to drive the vehicle manually.

python -m metadrive.examples.drive_in_single_agent_env

Run the following command to launch a safe driving scenario, which includes more complex obstacles and cost to be yielded.

python -m metadrive.examples.drive_in_safe_metadrive_env

You can also launch an instance of Multi-Agent scenario as follows

python -m metadrive.examples.drive_in_multi_agent_env --env roundabout

or launch and render in pygame front end

python -m metadrive.examples.drive_in_multi_agent_env --pygame_render --env roundabout

env argument could be:

• roundabout (default)
• intersection
• tollgate
• bottleneck
• parkinglot
• pgmap

Run the example of procedural generation of a new map as:

python -m metadrive.examples.procedural_generation

Note that the above four scripts can not be ran in a headless machine. Please refer to the installation guideline in documentation for more information about how to launch runing in a headless machine.

Run the following command to draw the generated maps from procedural generation:

python -m metadrive.examples.draw_maps

To build the RL environment in python script, you can simply code in the OpenAI gym format as:

import metadrive  # Import this package to register the environment!
import gym

env = gym.make("MetaDrive-v0", config=dict(use_render=True))
# env = metadrive.MetaDriveEnv(config=dict(environment_num=100))  # Or build environment from class
env.reset()
for i in range(1000):
    obs, reward, done, info = env.step(env.action_space.sample())  # Use random policy
    env.render()
    if done:
        env.reset()
env.close()

📦 Predefined environment sets

We define several standard MetaDrive Gym environments, where the user can start training off the shelf:

import gym
import metadrive  # Register the environment

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

The following table presents the names for the predefined environments.

Gym Environment Name	Random Seed Range	Number of Maps	Comments
MetaDrive-test-v0	[0, 200)	200	Test set, not change for all experiments.
MetaDrive-validation-v0	[200, 1000)	800	Validation set.
MetaDrive-v0	[1000, 1100)	100	Default training setting, for quick start.
MetaDrive-10envs-v0	[1000, 1100)	10	Training environment with 10 maps.
MetaDrive-1000envs-v0	[1000, 1100)	1000	Training environment with 1000 maps.
MetaDrive-training0-v0	[3000, 4000)	1000	First set of 1000 environments.
MetaDrive-training1-v0	[5000, 6000)	1000	Second set of 1000 environments.
MetaDrive-training2-v0	[7000, 8000)	1000	Thirds set of 1000 environments.
...			More map set and environments will be added

🏫 Documentations

Find more details in: MetaDrive

📎 References

Working in Progress!