sharpy-sc2
DI-star
sharpy-sc2 | DI-star | |
---|---|---|
2 | 9 | |
67 | 1,170 | |
- | 2.0% | |
5.1 | 3.2 | |
8 months ago | 7 days ago | |
Python | Python | |
MIT License | Apache License 2.0 |
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For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
sharpy-sc2
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sc2 bot noob questions
Hey all, I'm experimenting with writing an SC2 bot for the first time, using python-sc2 and sharpy-sc2. I've got it working and improved the default dumb zealot rush to be more efficient vs the dumb in-game AI. Two questions:
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A novel Terran strategy
If you know python you're already ahead of me. I expect you could make a bot in a few hours with sharpy-sc2.
DI-star
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Better AI ?
There is an AI/bot scene for SC2, I don't have many links but you can start by looking here: https://github.com/opendilab/DI-star https://www.youtube.com/watch?v=fvQF-24IpXs (Harstem and uThermal both have more videos vs different bots).
- [ENG] 2022 GSL S3 Code S RO.20 Group B
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Any idea about DI-star ? It's an AI model could beat top human players in StarCraft II!
Looks like a simplified AlphaStar using LSTM RNN instead of Pointer Transformer, much heavier supervised imitation learning, Zerg vs Zerg only (with simplified build order module), and a much smaller AlphaStar League: https://github.com/opendilab/DI-star/blob/main/docs/guidance_to_small_scale_training.md
For more information,plz visit out GitHub page:https://github.com/opendilab/DI-star
- Any idea about DI-star?An AI model could beat top human players in StarCraftII
- A large-scale game AI distributed training platform developed for StarCraftII
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Why can't we make a perfect AI for Starcraft through evolution
First of all, let's discuss what the level of AI is now. If the "level" refers to the capability of competing, the current AI has been very closed to the top human player in some types of games, like chess, Texas Poker, and Mahjong of CARDS, DOTA2 of MOBA, as well as StarCraft2 of RTS. As for other games, if we have enough human resources and computing performance, we also can get similar results. If the "level" has other meanings, like AI agents having human behavior, intelligent NPC can be designed specifically for different people so that they can have different gaming experience. These are all at the stage of issue-defining and exploring new technology solutions. Although traditional game AI is mostly based on hard code, it still has much prior knowledge. In recent years, some hot ML-related techs have performed well in competitiveness while in other fields, they haven't found the perfect entry point. If we expand the conclusions above in detail, the design of game AI can be divided into two parts: issue defining and issue solving. For those competitive issues which have already got complete definitions, their core issue is to explore the optimal strategy based on the evaluation standard, like ladder points. Traditional solutions can deal with less complex scenarios, like chess and Gobang. While machine learning related techs, including deep learning and reinforcement learning, they can perform very well in much more complex games, like StarCraft II. {For this you can try it in DI-star: this project is a reimplementation (with a few improvements) of Alphastar (Only Zerg vs Zerg) based on OpenDILab.}
- Show HN: Come and fight professional AI in StarCraftII
- DI-Star (Starcraft 2 AI, Continuation of AlphaStar)
What are some alternatives?
sc2-pulse - The fastest and most reliable ranked ladder tracker for StarCraft2 (Spring Boot app)
pytorch-lightning - The lightweight PyTorch wrapper for high-performance AI research. Scale your models, not the boilerplate. [Moved to: https://github.com/PyTorchLightning/pytorch-lightning]
python-sc2 - A StarCraft II bot api client library for Python 3
RobustVideoMatting - Robust Video Matting in PyTorch, TensorFlow, TensorFlow.js, ONNX, CoreML!
cloud-morph - Decentralize, Self-host Cloud Gaming/Application
thinc - 🔮 A refreshing functional take on deep learning, compatible with your favorite libraries
sc2reader - Extracts gameplay information from Starcraft II replay files
pytorch-lightning - Build high-performance AI models with PyTorch Lightning (organized PyTorch). Deploy models with Lightning Apps (organized Python to build end-to-end ML systems). [Moved to: https://github.com/Lightning-AI/lightning]
mini-AlphaStar - (JAIR'2022) A mini-scale reproduction code of the AlphaStar program. Note: the original AlphaStar is the AI proposed by DeepMind to play StarCraft II. JAIR = Journal of Artificial Intelligence Research.
Kornia - Geometric Computer Vision Library for Spatial AI
polyaxon - MLOps Tools For Managing & Orchestrating The Machine Learning LifeCycle
Stanza - Stanford NLP Python library for tokenization, sentence segmentation, NER, and parsing of many human languages