Movatterモバイル変換

[0]ホーム
URL:

Skip to content

Navigation Menu

Search code, repositories, users, issues, pull requests...

Provide feedback

We read every piece of feedback, and take your input very seriously.

Saved searches

Use saved searches to filter your results more quickly

 Sign up

A Real-Time-Strategy game for Deep Learning research

License

NotificationsYou must be signed in to change notification settings

cair/deep-rts

Repository files navigation

DescriptionBuild StatusDocumentationGitHub license

DeepRTS is a high-performance Real-TIme strategy game for Reinforcement Learning research.It is written in C++ for performance,but provides an python interface to better interface with machine-learning toolkits.Deep RTS can process the game with over6 000 000 steps per second and2 000 000 steps when rendering graphics.In comparison to other solutions, such as StarCraft, this is over15 000% faster simulation time running on Intel i7-8700k with Nvidia RTX 2080 TI.

The aim of Deep RTS is to bring a more affordable and sustainable solution to RTS AI research by reducing computation time.

It is recommended to use the master-branch for the newest (and usually best) version of the environment. I am greatful for any input in regards to improving the environment.

Please use the following citation when using this in your work!

@INPROCEEDINGS{8490409,author={P. {Andersen} and M. {Goodwin} and O. {Granmo}},booktitle={2018 IEEE Conference on Computational Intelligence and Games (CIG)},title={Deep RTS: A Game Environment for Deep Reinforcement Learning in Real-Time Strategy Games},year={2018},volume={},number={},pages={1-8},keywords={computer games;convolution;feedforward neural nets;learning (artificial intelligence);multi-agent systems;high-performance RTS game;artificial intelligence research;deep reinforcement learning;real-time strategy games;computer games;RTS AIs;Deep RTS game environment;StarCraft II;Deep Q-Network agent;cutting-edge artificial intelligence algorithms;Games;Learning (artificial intelligence);Machine learning;Planning;Ground penetrating radar;Geophysical measurement techniques;real-time strategy game;deep reinforcement learning;deep q-learning},doi={10.1109/CIG.2018.8490409},ISSN={2325-4270},month={Aug},}

Dependencies

  • Python >= 3.9.1

Installation

Method 1 (From Git Repo)

sudo pip3 install git+https://github.com/cair/DeepRTS.git

Method 2 (Clone & Build)

git clone https://github.com/cair/deep-rts.gitcd deep-rtsgit submodule syncgit submodule update --initsudo pip3 install .

Available maps

10x10-2-FFA15x15-2-FFA21x21-2-FFA31x31-2-FFA31x31-4-FFA31x31-6-FFA

Scenarios

Deep RTS features scenarios which is pre-built mini-games.These mini-games is well suited to train agents on specific tasks,or to test algorithms in different problem setups.The benefits of using scenarios is that you can trivially design reward functions using criterias that each outputs a reward/punishment signal depending on completion of the task.Examples of tasks are to:

  • collect 1000 gold
  • do 100 damage
  • take 1000 damage
  • defeat 5 enemies

Deep RTS currently implements the following scenarios

GoldCollectFifteenGeneralAIOneVersusOne

Minimal Example

importrandomfromDeepRTS.pythonimportConfigfromDeepRTS.pythonimportscenarioif__name__=="__main__":random_play=Trueepisodes=100foriinrange(episodes):env=scenario.GeneralAI_1v1(Config.Map.THIRTYONE)state=env.reset()done=Falsewhilenotdone:env.game.set_player(env.game.players[0])action=random.randrange(15)next_state,reward,done,_=env.step(action)state=next_stateif (done):breakenv.game.set_player(env.game.players[1])action=random.randrange(15)next_state,reward,done,_=env.step(action)state=next_state

In-Game Footage

10x10 - 2 Player - free-for-all

15x15 - 2 Player - free-for-all

21x21 - 2 Player - free-for-all

31x31 - 2 Player - free-for-all

31x31 - 4 Player - free-for-all

31x3 - 6 Player - free-for-all

[8]ページ先頭
©2009-2025 Movatter.jp