Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

https://github.com/oxwhirl/smac

SMAC: The StarCraft Multi-Agent Challenge
https://github.com/oxwhirl/smac

benchmark machine-learning multiagent-systems reinforcement-learning starcraft-ii

Last synced: about 2 months ago
JSON representation

SMAC: The StarCraft Multi-Agent Challenge

Lists

README 

        


 




> __Note__

> SMACv2 is out! Check it out [here](https://github.com/oxwhirl/smacv2).



> __Warning__

> **Please pay attention to the version of SC2 used for your experiments.** Performance is **not** always comparable between versions. The results in the [SMAC paper](https://arxiv.org/abs/1902.04043) use `SC2.4.6.2.69232` not `SC2.4.10`.



# SMAC - StarCraft Multi-Agent Challenge



[SMAC](https://github.com/oxwhirl/smac) is [WhiRL](http://whirl.cs.ox.ac.uk)'s environment for research in the field of cooperative multi-agent reinforcement learning (MARL) based on [Blizzard](http://blizzard.com)'s [StarCraft II](https://en.wikipedia.org/wiki/StarCraft_II:_Wings_of_Liberty) RTS game. SMAC makes use of Blizzard's [StarCraft II Machine Learning API](https://github.com/Blizzard/s2client-proto) and [DeepMind](https://deepmind.com)'s [PySC2](https://github.com/deepmind/pysc2) to provide a convenient interface for autonomous agents to interact with StarCraft II, getting observations and performing actions. Unlike the [PySC2](https://github.com/deepmind/pysc2), SMAC concentrates on *decentralised micromanagement* scenarios, where each unit of the game is controlled by an individual RL agent.



Please refer to the accompanying [paper](https://arxiv.org/abs/1902.04043) and [blogpost](https://blog.ucldark.com/2019/02/12/smac.html) for the outline of our motivation for using SMAC as a testbed for MARL research and the initial experimental results.



## About



Together with SMAC we also release [PyMARL](https://github.com/oxwhirl/pymarl) - our [PyTorch](https://github.com/pytorch/pytorch) framework for MARL research, which includes implementations of several state-of-the-art algorithms, such as [QMIX](https://arxiv.org/abs/1803.11485) and [COMA](https://arxiv.org/abs/1705.08926).



Data from the runs used in the paper is included [here](https://github.com/oxwhirl/smac/releases/download/v1/smac_run_data.json). **These runs are outdated based on recent changes in StarCraft II. If you ran your experiments using the current version of SMAC, you mustn't compare your results with the ones provided here.**



# Quick Start



## Installing SMAC



You can install SMAC by using the following command:



```shell

pip install git+https://github.com/oxwhirl/smac.git

```



Alternatively, you can clone the SMAC repository and then install `smac` with its dependencies:



```shell

git clone https://github.com/oxwhirl/smac.git

pip install -e smac/

```



*NOTE*: If you want to extend SMAC, please install the package as follows:



```shell

git clone https://github.com/oxwhirl/smac.git

cd smac

pip install -e ".[dev]"

pre-commit install

```



You may also need to upgrade pip: `pip install --upgrade pip` for the install to work.



## Installing StarCraft II



SMAC is based on the full game of StarCraft II (versions >= 3.16.1). To install the game, follow the commands bellow.



### Linux



Please use the Blizzard's [repository](https://github.com/Blizzard/s2client-proto#downloads) to download the Linux version of StarCraft II. By default, the game is expected to be in `~/StarCraftII/` directory. This can be changed by setting the environment variable `SC2PATH`.



### MacOS/Windows



Please install StarCraft II from [Battle.net](https://battle.net). The free [Starter Edition](http://battle.net/sc2/en/legacy-of-the-void/) also works. PySC2 will find the latest binary should you use the default install location. Otherwise, similar to the Linux version, you would need to set the `SC2PATH` environment variable with the correct location of the game.



## SMAC maps



SMAC is composed of many combat scenarios with pre-configured maps. Before SMAC can be used, these maps need to be downloaded into the `Maps` directory of StarCraft II.



Download the [SMAC Maps](https://github.com/oxwhirl/smac/releases/download/v0.1-beta1/SMAC_Maps.zip) and extract them to your `$SC2PATH/Maps` directory. If you installed SMAC via git, simply copy the `SMAC_Maps` directory from `smac/env/starcraft2/maps/` into `$SC2PATH/Maps` directory.



### List the maps



To see the list of SMAC maps, together with the number of ally and enemy units and episode limit, run:



```shell

python -m smac.bin.map_list 

```



### Creating new maps



Users can extend SMAC by adding new maps/scenarios. To this end, one needs to:



- Design a new map/scenario using StarCraft II Editor:

  - Please take a close look at the existing maps to understand the basics that we use (e.g. Triggers, Units, etc),

  - We make use of special RL units which never automatically start attacking the enemy. [Here](https://docs.google.com/document/d/1BfAM_AtZWBRhUiOBcMkb_uK4DAZW3CpvO79-vnEOKxA/edit?usp=sharing) is the step-by-step guide on how to create new RL units based on existing SC2 units,

- Add the map information in [smac_maps.py](https://github.com/oxwhirl/smac/blob/master/smac/env/starcraft2/maps/smac_maps.py),

- The newly designed RL units have new ids which need to be handled in [starcraft2.py](https://github.com/oxwhirl/smac/blob/master/smac/env/starcraft2/starcraft2.py). Specifically, for heterogenious maps containing more than one unit types, one needs to manually set the unit ids in the `_init_ally_unit_types()` function.



## Testing SMAC



Please run the following command to make sure that `smac` and its maps are properly installed. 



```bash

python -m smac.examples.random_agents

```



## Saving and Watching StarCraft II Replays



### Saving a replay



If you’ve using our [PyMARL](https://github.com/oxwhirl/pymarl) framework for multi-agent RL, here’s what needs to be done:

1. **Saving models**: We run experiments on *Linux* servers with `save_model = True` (also `save_model_interval` is relevant) setting so that we have training checkpoints (parameters of neural networks) saved (click [here](https://github.com/oxwhirl/pymarl#saving-and-loading-learnt-models) for more details).

2. **Loading models**: Learnt models can be loaded using the `checkpoint_path` parameter. If you run PyMARL on *MacOS* (or *Windows*) while also setting `save_replay=True`, this will save a .SC2Replay file for `test_nepisode` episodes on the test mode (no exploration) in the Replay directory of StarCraft II. (click [here](https://github.com/oxwhirl/pymarl#watching-starcraft-ii-replays) for more details).



If you want to save replays without using PyMARL, simply call the `save_replay()` function of SMAC's StarCraft2Env in your training/testing code. This will save a replay of all epsidoes since the launch of the StarCraft II client.



The easiest way to save and later watch a replay on Linux is to use [Wine](https://www.winehq.org/).



### Watching a replay



You can watch the saved replay directly within the StarCraft II client on MacOS/Windows by *clicking on the corresponding Replay file*.



You can also watch saved replays by running:



```shell

python -m pysc2.bin.play --norender --replay 

```



This works for any replay as long as the map can be found by the game. 



For more information, please refer to [PySC2](https://github.com/deepmind/pysc2) documentation.



# Documentation 



For the detailed description of the environment, read the [SMAC documentation](docs/smac.md). 



The initial results of our experiments using SMAC can be found in the [accompanying paper](https://arxiv.org/abs/1902.04043).



# Citing  SMAC 



If you use SMAC in your research, please cite the [SMAC paper](https://arxiv.org/abs/1902.04043).



*M. Samvelyan, T. Rashid, C. Schroeder de Witt, G. Farquhar, N. Nardelli, T.G.J. Rudner, C.-M. Hung, P.H.S. Torr, J. Foerster, S. Whiteson. The StarCraft Multi-Agent Challenge, CoRR abs/1902.04043, 2019.*



In BibTeX format:



```tex

@article{samvelyan19smac,

  title = {{The} {StarCraft} {Multi}-{Agent} {Challenge}},

  author = {Mikayel Samvelyan and Tabish Rashid and Christian Schroeder de Witt and Gregory Farquhar and Nantas Nardelli and Tim G. J. Rudner and Chia-Man Hung and Philiph H. S. Torr and Jakob Foerster and Shimon Whiteson},

  journal = {CoRR},

  volume = {abs/1902.04043},

  year = {2019},

}

```



# Code Examples



Below is a small code example which illustrates how SMAC can be used. Here, individual agents execute random policies after receiving the observations and global state from the environment.  



If you want to try the state-of-the-art algorithms (such as [QMIX](https://arxiv.org/abs/1803.11485) and [COMA](https://arxiv.org/abs/1705.08926)) on SMAC, make use of [PyMARL](https://github.com/oxwhirl/pymarl) - our framework for MARL research.



```python

from smac.env import StarCraft2Env

import numpy as np



def main():

    env = StarCraft2Env(map_name="8m")

    env_info = env.get_env_info()



    n_actions = env_info["n_actions"]

    n_agents = env_info["n_agents"]



    n_episodes = 10



    for e in range(n_episodes):

        env.reset()

        terminated = False

        episode_reward = 0



        while not terminated:

            obs = env.get_obs()

            state = env.get_state()

            # env.render()  # Uncomment for rendering



            actions = []

            for agent_id in range(n_agents):

                avail_actions = env.get_avail_agent_actions(agent_id)

                avail_actions_ind = np.nonzero(avail_actions)[0]

                action = np.random.choice(avail_actions_ind)

                actions.append(action)



            reward, terminated, _ = env.step(actions)

            episode_reward += reward



        print("Total reward in episode {} = {}".format(e, episode_reward))



    env.close()



```



## RLlib Examples



You can also run SMAC environments in [RLlib](https://rllib.io), which includes scalable algorithms such as [PPO](https://ray.readthedocs.io/en/latest/rllib-algorithms.html#proximal-policy-optimization-ppo) and [IMPALA](https://ray.readthedocs.io/en/latest/rllib-algorithms.html#importance-weighted-actor-learner-architecture-impala). Check out the example code [here](https://github.com/oxwhirl/smac/tree/master/smac/examples/rllib).



## PettingZoo Example



Thanks to [Rodrigo de Lazcano](https://github.com/rodrigodelazcano), SMAC now supports [PettingZoo API](https://github.com/PettingZoo-Team/PettingZoo) and PyGame environment rendering. Check out the example code [here](https://github.com/oxwhirl/smac/tree/master/smac/examples/pettingzoo).