metadrive-simulator 0.2.2

An open-ended driving simulator with infinite scenes

MetaDrive: Composing Diverse Driving Scenarios for Generalizable RL

[ Website | Documentation | Github Repo ]

Welcome to MetaDrive! MetaDrive is an driving simulator with many key features:

• Lightweight: Extremely easy to download, install and run in almost all platforms. Up to 300 simulation step per second and easy to parallel.
• Realistic: Accurate physics simulation and multiple sensory input including Lidar, sensory data, top-down semantic map and first-person view images.
• Compositional: Support generating infinite scenes and configuring various traffics, vehicles, and environmental settings.

🛠 Quick Start

Please 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 and efficiency of MetaDrive via running:

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

The above script is supposed to be runnable in all places. Note that please do not run the above command in the folder that has a sub-folder called ./metadrive.

🚕 Examples

Please run the following command to drive the car in the environment manually with keyboard! Pressing W, A, S, D to move and turn and T to trigger the auto-drive mode!

python -m metadrive.examples.drive_in_single_agent_env

Besides, a scenario where safety must be concerned is included in examples, and this environment also shows how to compose a sophisticate scenario.

python -m metadrive.examples.drive_in_safe_metadrive_env

You can also drive in our Multi-agent RL environment.

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

Accepted arguments:

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

To show the main feature, procedural generation, we provide a script to show BIG:

python -m metadrive.examples.procedural_generation

Note that the above three scripts can not be run in headless machine. Please refer to the installation guideline in documentation for more information.

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

python -m metadrive.examples.draw_maps

To build the environment in python script, you can simply run:

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 also define several Gym environment names, so user can start training in the minimalist manner:

import gym
import metadrive  # Register the environment

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

The following table presents some predefined environment names.

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 can be added in response to the requests

🏫 Documentations

Find more detail in: MetaDrive

📎 Citation

Working in Progress!