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A simple and highly efficient RTS-game-inspired environment for reinforcement learning

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microRTS is a small implementation of an RTS game, designed to perform AI research. The advantage of using microRTS with respect to using a full-fledged game like Wargus or StarCraft (using BWAPI) is that microRTS is much simpler, and can be used to quickly test theoretical ideas, before moving on to full-fledged RTS games.

By default, microRTS is deterministic and real-time (i.e. players can issue actions simultaneously, and actions are durative). However, it is possible to experiment both with fully-observable and partially-observable games, as well as with deterministic and non-deterministic settings via configuration flags. As part of the implementation, I include a collection of hard-coded, and game-tree search techniques (such as variants of minimax, Monte Carlo search, and Monte Carlo Tree Search).

microRTS was developed by Santiago Ontañón.

MicroRTS-Py will eventually be updated, maintained, and made compliant with the standards of the Farama Foundation (https://farama.org/project_standards). However, this is currently a lower priority than other projects we're working to maintain. If you'd like to contribute to development, you can join our discord server here- https://discord.gg/jfERDCSw.

For a video of how microRTS looks like when a human plays, see a YouTube video

If you are interested in testing your algorithms against other people's, there is an annual microRTS competition. For more information on the competition see the competition website. The previous competitions have been organized at IEEE-CIG2017 and IEEE-CIG2018, and this year it's organized at IEEE-COG2019 (notice the change of name of the conference).

To cite microRTS, please cite this paper:

Santiago Ontañón (2013) The Combinatorial Multi-Armed Bandit Problem and its Application to Real-Time Strategy Games, In AIIDE 2013. pp. 58 - 64.

Setting up microRTS in an IDE

Watch this YouTube video to learn how to acquire microRTS and setup a project using Netbeans.

Reinforcement Learning in microRTS

If you'd like to use reinforcement learning in microRTS please check this project: https://github.com/Farama-Foundation/MicroRTS-Py

Executing microRTS through the terminal

If you want to build and run microRTS from source using the command line, clone or download this repository and run the following commands in the root folder of the project to compile the source code:

Linux or Mac OS:

javac -cp "lib/*:src" -d bin src/rts/MicroRTS.java # to build

Windows:

javac -cp "lib/*;src" -d bin src/rts/MicroRTS.java # to build

Generating a JAR file

You can join all compiled source files and dependencies into a single JAR file, which can be executed on its own. In order to create a JAR file for microRTS:

javac -cp "lib/*:src" -d bin $(find . -name "*.java") # compile source files
cd bin
find ../lib -name "*.jar" | xargs -n 1 jar xvf # extract the contents of the JAR dependencies
jar cvf microrts.jar $(find . -name '*.class' -type f) # create a single JAR file with sources and dependencies

Executing microRTS

To execute microRTS from compiled class files:

java -cp "lib/*:bin" rts.MicroRTS # on Linux/Mac
java -cp "lib/*;bin" rts.MicroRTS # on Windows

To execute microRTS from the JAR file:

java -cp microrts.jar rts.MicroRTS

Which class to execute

microRTS has multiple entry points, and for experimentation purposes you might eventually want to create your own class if none of the base ones suit your needs (see the "tests" folder for examples), but a default one is the gui.frontend.FrontEnd class, which opens the default GUI. To execute microRTS in this way, use the following command:

java -cp microrts.jar gui.frontend.FrontEnd

Another, more expansive entry point is the rts.MicroRTS class. It is capable of starting microRTS in multiple modes, such as in client mode (attempts to connect to a server which will provide commands to a bot), server mode (tries to connect to a client in order to control a bot), run a standalone game and exit or open the default GUI.

The rts.MicroRTS class accepts multiple initialization parameters, either from the command line or from a properties file. A list of all the acceptable command-line arguments can be accessed through the following command:

java -cp microrts.jar rts.MicroRTS -h

An example of a properties file is provided in the resources directory. microRTS can be started using a properties file with the following command:

java -cp microrts.jar rts.MicroRTS -f my_file.properties

Instructions

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A simple and highly efficient RTS-game-inspired environment for reinforcement learning

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