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A simple and highly efficient RTS-game-inspired environment for reinforcement learning
| Date | Stars |
|---|---|
| 2026-07-31 | 357 |
| 2026-08-03 | 357 |
| 2026-08-04 | 357 |
| 2026-08-06 | 357 |
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<p align="center">
<img src="microrts-text.png" width="500px"/>
</p>
**Aug 11, 2025: This project has been deprecated due to a lack of wide spread community use, and is no longer planned to receive any additional updates or support.**
[](https://travis-ci.org/douglasrizzo/microrts)
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](https://sites.google.com/site/santiagoontanonvillar/Home).
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](https://www.youtube.com/watch?v=ZsKKAoiD7B0)
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](https://sites.google.com/site/micrortsaicompetition/home). 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](https://www.youtube.com/watch?v=_jVOMNqw3Qs) 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:
```shell
javac -cp "lib/*:src" -d bin src/rts/MicroRTS.java # to build
```
Windows:
```shell
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:
```shell
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:
```shell
java -cp "lib/*:bin" rts.MicroRTS # on Linux/Mac
java -cp "lib/*;bin" rts.MicroRTS # on Windows
```
To execute microRTS from the JAR file:
```shell
java -cp microrts.jar rts.MicroRTS
```
#### Which class to execute
microRTS has multiple entry points, and for expeExcerpt of 5,308 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:ea8f565f25e934c6, llm:Description: 'A simple and highly efficient RTS-game-inspired environment for reinforcement learning' (repository description). Language: Java. No topics provided.
matched fp:ea8f565f25e934c6, llm:Description: 'A simple and highly efficient RTS-game-inspired environment for reinforcement learning' (repository description). Language: Java. No topics provided.
matched fp:ea8f565f25e934c6, llm:Description: 'A simple and highly efficient RTS-game-inspired environment for reinforcement learning' (repository description). Language: Java. No topics provided.