simsmart 0.4.16
Scalable Multi-Agent RL Training School
Requires Python: >=3.7

# SMARTS
![SMARTS CI](https://github.com/junluo-huawei/SMARTS/workflows/SMARTS%20CI/badge.svg?branch=master) ![Code style](https://img.shields.io/badge/code%20style-black-000000.svg)

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](https://github.com/huawei-noah/xingtian/) suite of RL platforms from Huawei Noah's Ark Lab.

![](docs/_static/smarts_envision.gif)

## Multi-Agent experiment as simple as...

```python
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

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

# git clone ...
cd <project>

# 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](http://supervisord.org/introduction.html) to run SMARTS together with it's supporting processes. To run the default example simply build a scenario and start supervisord:

```bash
# 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](./envision/README.md) for more information on Envision, our front-end visualization tool.

Several example scripts are provided under [`SMARTS/examples`](./examples), as well as a handful of scenarios under [`SMARTS/scenarios`](./scenarios). You can create your own scenarios using the [Scenario Studio](./smarts/sstudio). Here's how you can use one of the example scripts with a scenario.

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

## 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/](./examples) directory.

## Contributing

Please read [Contributing](CONTRIBUTING.md)

### Building Docs Locally

Assuming you have run `pip install .[dev]`.

```bash
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](https://github.com/facebookresearch/visdom) integration. Start the visdom server before running your scenario,

```bash
visdom
# Open the printed URL in your browser
```

And in your experiment, start your environment with `visdom=True`

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

### Interfacing w/ PyMARL and malib

[PyMARL](https://github.com/oxwhirl/pymarl) and [malib](https://github.com/ying-wen/malib) have been open-sourced. You can run them via,

```bash
git clone git@github.com: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
```

```bash
git clone git@github.com: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,

```bash
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:v0.4.3-pre
# <press enter>

# Run Envision server in the background
$ scl envision start -s ./scenarios -p 8081 &
$ python examples/single_agent.py scenarios/loop

# On your most 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](https://hub.docker.com/orgs/huaweinoah) run,

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

export VERSION=v0.4.3-pre
docker build -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](docs/setup.rst)** and **[SUMO TROUBLESHOOTING](docs/SUMO_TROUBLESHOOTING.md)**
* If you wish to find binaries: **[SUMO Download Page](https://sumo.dlr.de/docs/Downloads.php)**
* If you wish to compile from source see: **[SUMO Build Instructions](https://sumo.dlr.de/docs/Developer/Main.html#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](https://github.com/eclipse/sumo/commits/v1_7_0)** or higher

### Citing SMARTS

If you use SMARTS in your research, please cite the [paper](https://arxiv.org/abs/2010.09776). In BibTeX format:

```bibtex
@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}
}
```