Need help with SMARTS?
Click the “chat” button below for chat support from the developer who created it, or find similar developers for support.

About the developer

huawei-noah
319 Stars 71 Forks MIT License 325 Commits 159 Opened issues

Description

Scalable Multi-Agent RL Training School for Autonomous Driving

Services available

!
?

Need anything else?

Contributors list

# 38,621
aspect-...
Objecti...
iOS
cocoapo...
123 commits
# 413,933
Python
Shell
Jupyter...
HTML
41 commits
# 113,238
Rust
kafka
Elixir
Erlang
20 commits
# 289,338
Python
Jupyter...
sars-co...
Shell
4 commits
# 140,487
Deep le...
C++
C
Shell
1 commit

SMARTS

SMARTS CI Code style

SMARTS (Scalable Multi-Agent RL Training School) is a simulation platform for reinforcement learning and multi-agent research on autonomous driving. Its focus is on realistic and diverse interactions. It is part of the XingTian suite of RL platforms from Huawei Noah's Ark Lab.

Check out the paper at SMARTS: Scalable Multi-Agent Reinforcement Learning Training School for Autonomous Driving for background on some of the project goals.

Multi-Agent experiment as simple as...

import gym

from smarts.core.agent_interface import AgentInterface, AgentType from smarts.core.agent import AgentSpec, Agent

class SimpleAgent(Agent): def act(self, obs): return "keep_lane"

agent_spec = AgentSpec( interface=AgentInterface.from_type(AgentType.Laner, max_episode_steps=None), agent_builder=SimpleAgent, )

agent_specs = { "Agent-007": agent_spec, "Agent-008": agent_spec, }

env = gym.make( "smarts.env:hiway-v0", scenarios=["scenarios/loop"], agent_specs=agent_specs, )

agents = { agent_id: agent_spec.build_agent() for agent_id, agent_spec in agent_specs.items() } observations = env.reset()

for _ in range(1000): agent_actions = { agent_id: agents[agent_id].act(agent_obs) for agent_id, agent_obs in observations.items() } observations, _, _, _ = env.step(agent_actions)

Setup

# For Mac OS X users, make sure XQuartz is pre-installed as SUMO's dependency

git clone ...

cd

Follow the instructions given by prompt for setting up the SUMO_HOME environment variable

./install_deps.sh

verify sumo is >= 1.5.0

if you have issues see ./doc/SUMO_TROUBLESHOOTING.md

sumo

setup virtual environment; presently only Python 3.7.x is officially supported

python3.7 -m venv .venv

enter virtual environment to install all dependencies

source .venv/bin/activate

upgrade pip, a recent version of pip is needed for the version of tensorflow we depend on

pip install --upgrade pip

install [train] version of python package with the rllib dependencies

pip install -e .[train]

make sure you can run tests (and verify they are passing)

pip install -e .[test] make test

then you can run a scenario, see following section for more details

Running

We use supervisord to run SMARTS together with it's supporting processes. To run the default example simply build a scenario and start supervisord:

# build scenarios/loop
scl scenario build --clean scenarios/loop

start supervisord

supervisord

With

supervisord
running, visit http://localhost:8081/ in your browser to view your experiment.

See ./envision/README.md for more information on Envision, our front-end visualization tool.

Several example scripts are provided under

SMARTS/examples
, as well as a handful of scenarios under
SMARTS/scenarios
. You can create your own scenarios using the Scenario Studio. Here's how you can use one of the example scripts with a scenario.

# Update the command=... in ./supervisord.conf
#
# [program:smarts]
# command=python examples/single_agent.py scenarios/loop
# ...

Documentation

Documentation is available at smarts.readthedocs.io

CLI tool

SMARTS provides a command-line tool to interact with scenario studio and Envision.

Usage

scl COMMAND SUBCOMMAND [OPTIONS] [ARGS]...

Commands: * envision * scenario * zoo

Subcommands of scenario: * build-all: Generate all scenarios under the given directories * build: Generate a single scenario * clean: Clean generated artifacts

Subcommands of envision: * start: start envision server

Subcommands of zoo: * zoo: Build an agent, used for submitting to the agent-zoo

Examples:

# Start envision, serve scenario assets out of ./scenarios
scl envision start --scenarios ./scenarios

Build all scenario under given directories

scl scenario build-all ./scenarios ./eval_scenarios

Rebuild a single scenario, replacing any existing generated assets

scl scenario build --clean scenarios/loop

Clean generated scenario artifacts

scl scenario clean scenarios/loop

Interfacing with Gym

See the provided ready-to-go scripts under the examples/ directory.

Contributing

Please read Contributing

Bug reports

Please read how to create a bug report and then open an issue here.

Building Docs Locally

Assuming you have run

pip install .[dev]
.
make docs

python -m http.server -d docs/_build/html

Open http://localhost:8000 in your browser

Extras

Visualizing Agent Observations

If you want to easily visualize observations you can use our Visdom integration. Start the visdom server before running your scenario,

visdom
# Open the printed URL in your browser

And in your experiment, start your environment with

visdom=True
env = gym.make(
    "smarts.env:hiway-v0",
    scenarios=["scenarios/loop"],
    agent_specs=agent_specs,
    visdom=True,
)

Interfacing w/ PyMARL and malib

PyMARL and malib have been open-sourced. You can run them via,

git clone [email protected]:ying-wen/pymarl.git

ln -s your-project/scenarios ./pymarl/scenarios

cd pymarl

setup virtual environment

python3 -m venv .venv source .venv/bin/activate pip install -r requirements.txt

python src/main.py --config=qmix --env-config=smarts

git clone [email protected]:ying-wen/malib.git

ln -s your-project/scenarios ./malib/scenarios

cd malib

setup virtual environment

python3 -m venv .venv source .venv/bin/activate pip install -r requirements.txt

python examples/run_smarts.py --algo SAC --scenario ./scenarios/loop --n_agents 5

Using Docker

If you're comfortable using docker or are on a platform without suitable support to easily run SMARTS (e.g. an older version of Ubuntu) you can run the following,

$ cd /path/to/SMARTS
$ docker run --rm -it -v $PWD:/src -p 8081:8081 huaweinoah/smarts:
# E.g. docker run --rm -it -v $PWD:/src -p 8081:8081 huaweinoah/smarts:v0.4.12
# 

Run Envision server in the background

This will only need to be run if you want visualisation

$ scl envision start -s ./scenarios -p 8081 &

Build an example

This needs to be done the first time and after changes to the example

$ scl scenario build scenarios/loop --clean

Run an example

add --headless if you do not need visualisation

$ python examples/single_agent.py scenarios/loop

On your host machine visit http://localhost:8081 to see the running simulation in

Envision.

(For those who have permissions:) if you want to push new images to our public dockerhub registry run,

# For this to work, your account needs to be added to the huaweinoah org
docker login

export VERSION=v0.4.3-pre docker build --no-cache -t smarts:$VERSION . docker tag smarts:$VERSION huaweinoah/smarts:$VERSION docker push huaweinoah/smarts:$VERSION

Troubleshooting

General

In many cases additinal run logs are located at '~/.smarts'. These can sometimes be helpful.

SUMO

SUMO can have some problems in setup. Please look through the following for support for SUMO: * If you are having issues see: SETUP and SUMO TROUBLESHOOTING * If you wish to find binaries: SUMO Download Page * If you wish to compile from source see: SUMO Build Instructions. * Please note that building SUMO may not install other vital dependencies that SUMO requires to run. * If you build from the git repository we recommend you use: SUMO version 1.7.0 or higher

Citing SMARTS

If you use SMARTS in your research, please cite the paper. In BibTeX format:

@misc{zhou2020smarts,
      title={SMARTS: Scalable Multi-Agent Reinforcement Learning Training School for Autonomous Driving},
      author={Ming Zhou and Jun Luo and Julian Villella and Yaodong Yang and David Rusu and Jiayu Miao and Weinan Zhang and Montgomery Alban and Iman Fadakar and Zheng Chen and Aurora Chongxi Huang and Ying Wen and Kimia Hassanzadeh and Daniel Graves and Dong Chen and Zhengbang Zhu and Nhat Nguyen and Mohamed Elsayed and Kun Shao and Sanjeevan Ahilan and Baokuan Zhang and Jiannan Wu and Zhengang Fu and Kasra Rezaee and Peyman Yadmellat and Mohsen Rohani and Nicolas Perez Nieves and Yihan Ni and Seyedershad Banijamali and Alexander Cowen Rivers and Zheng Tian and Daniel Palenicek and Haitham bou Ammar and Hongbo Zhang and Wulong Liu and Jianye Hao and Jun Wang},
      year={2020},
      eprint={2010.09776},
      archivePrefix={arXiv},
      primaryClass={cs.MA}
}

We use cookies. If you continue to browse the site, you agree to the use of cookies. For more information on our use of cookies please see our Privacy Policy.