SMARTS

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 <path/to/SMARTS>

# Follow the instructions given by prompt for setting up the SUMO_HOME environment variable
bash utils/setup/install_deps.sh

# verify sumo is >= 1.5.0
# if you have issues see ./doc/SUMO_TROUBLESHOOTING.md
sumo

# setup virtual environment; presently at least Python 3.7 and higher 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 to install [test] version of python package with the rllib dependencies so that you can run sanity-test (and verify they are passing)
# OPTIONAL: install [camera-obs] version of python package with the panda3D dependencies if you want to render camera sensor observations in your simulations
pip install -e .[camera-obs]  

# make sure you can run sanity-test (and verify they are passing)
# if tests fail, check './sanity_test_result.xml' for test report. 
pip install -e .[test]
make sanity-test

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

Running

We use the scl command line to run SMARTS together with it's supporting processes. To run the default example simply build a scenario and run the following command:

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

# run an experiment 
scl run --envision examples/single_agent.py scenarios/loop 

You need to add the --envision flag to run the Envision server where you can see the visualization of the experiment. See ./envision/README.md for more information on Envision, our front-end visualization tool.

After executing the above command, visit http://localhost:8081/ in your browser to view your experiment.

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. Below is the generic command to run and visualize one of the example scripts with a scenario.

scl run --envision <examples/script_path> <scenarios/path> 

Pass in the agent example path and scenarios folder path above to run an experiment like the one mentioned above.

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:

• scenario
• envision
• zoo
• run

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

Subcommands of run: No subcommands of run. You can directly use run to simulate an experiment as mentioned in the example above.

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:<version>
# E.g. docker run --rm -it -v $PWD:/src -p 8081:8081 huaweinoah/smarts:v0.4.18
# <press enter>

# 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
$ cd </path/to/SMARTS>
export VERSION=v0.4.18
$ docker build --no-cache -f ./utils/docker/Dockerfile -t huaweinoah/smarts:$VERSION .
$ docker login
$ docker push huaweinoah/smarts:$VERSION

Using Singularity

$ cd </path/to/SMARTS>

# Build container from definition file.
$ sudo singularity build ./utils/singularity/smarts.sif ./utils/singularity/smarts.def

# Use the container to build the required scenarios.
$ singularity shell --containall --bind ../SMARTS:/src ./utils/singularity/smarts.sif
# Inside the container
Singularity> scl scenario build /src/scenarios/loop/
Singularity> exit

# Then, run the container using one of the following methods.

# 1. Run container in interactive mode.
$ singularity shell --containall --bind ../SMARTS:/src ./utils/singularity/smarts.sif
# Inside the container
Singularity> python3.7 /src/examples/single_agent.py /src/scenarios/loop/ --headless

# 2. Run commands within the container from the host system.
$ singularity exec --containall --bind ../SMARTS:/src ./utils/singularity/smarts.sif python3.7 /src/examples/single_agent.py /src/scenarios/loop/ --headless

# 3. Run container instance in the background.
$ singularity instance start --containall --bind ../SMARTS:/src ./utils/singularity/smarts.sif smarts_train /src/examples/single_agent.py /src/scenarios/loop/ --headless

Troubleshooting

General

In most cases SMARTS debug logs are located at ~/.smarts. These can be helpful to diagnose problems.

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},
      url={https://arxiv.org/abs/2010.09776},
      primaryClass={cs.MA},
      booktitle={Proceedings of the 4th Conference on Robot Learning (CoRL)},
      year={2020},
      month={11}
 }