This is a repository for the SMAClite environment. It is a (nearly) pure Python reimplementation of the Starcraft Multi-Agent Challenge, using Numpy and OpenAI Gym.

The main features of this environment include:

• A fully functional Python implementation of the SMAC environment
• A JSON interface for defining units and scenarios
• Compatibility with the OpenAI Gym API
• (optional) a highly-performant C++ implementation of the collision avoidance algorithm

The following units are available in this environment:

• baneling
• colossus
• marauder
• marine
• medivac
• spine crawler
• stalker
• zealot
• zergling

The following scenarios are available in this environment:

• 10m_vs_11m
• 27m_vs_30m
• 2c_vs_64zg
• 2s3z
• 2s_vs_1sc
• 3s5z
• 3s5z_vs_3s6z
• 3s_vs_5z
• bane_vs_bane
• corridor
• mmm
• mmm2

Note that further scenarios can easily be added by modifying or creating a scenario JSON file.

Run

pip install .

In the SMAClite directory

As far as we are aware, this project fully adheres to the OpenAI Gym API, so it can be used with any framework capable of interfacing with Gym-capable environments. We recommend the ePyMARL framework, made available in our repository. EPyMARL uses yaml files to specify run configurations. To train a model in the MMM2 scenario using the MAPPO algorithm, you can use this example command:

python3 src/main.py --config=mappo --env-config=gymma with seed=1 env_args.time_limit=120 env_args.key="smaclite:smaclite/MMM2-v0

Note that to use the C++ version of the collision avoidance algorithm, you will have to add the line use_cpp_rvo2: true to the yaml config file you're referencing, since Sacred does not allow defining new config entries in the command itself.