# opendilab/DI-star

An artificial intelligence platform for the StarCraft II with large-scale distributed training and grand-master agents.

Repository: https://github.com/opendilab/DI-star
Canonical: https://ross.abutalabs.com/products/di-star
Language: Python
License: Apache-2.0
License Family: permissive
Topics: reinforcment-learning, starcraft2, self-play, artificial-intelligence, deep-learning, league, deep-reinforcement-learning
Last push: 2025-03-13T10:36:57+00:00

## Health v2 (maintenance only)
Score: 37/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 11, release rhythm 35, longevity 100
- inputs: {"age_days": 1886, "days_push": 538, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1393, forks 127 (observed 2026-08-28T04:04:36.256652+00:00)

## What it is
DI-star is a large-scale distributed training platform for building StarCraft II game AI, including supervised and reinforcement learning training code and pre-trained grand-master-level Zerg agents. It lets researchers train agents via self-play and play against the released models.

## Use cases
- train a StarCraft II AI agent with reinforcement learning
- run large-scale self-play distributed training for game AI
- play against a pre-trained grand-master StarCraft II bot
- reproduce AlphaStar-style league training on a budget
- do supervised learning from StarCraft II replays
- research multi-agent reinforcement learning in a real-time strategy game

## When to choose
- you want to train or evaluate deep RL agents specifically for StarCraft II
- you need a reference implementation of AlphaStar-style league/self-play training
- you want to play against a strong pre-trained SC2 agent

## When to avoid
- you need a general-purpose RL library not tied to StarCraft II
- you want agents for other StarCraft II races than Zerg vs Zerg
- you lack a GPU or a StarCraft II installation
- you need actively developed features or newer game patch support

## Facets
- artifact type: framework
- maturity: maintenance
- function: reinforcement-learning, deep-learning, machine-learning, agent-framework, llm-training
- domain: reinforcement-learning, artificial-intelligence, deep-learning, gaming-tools
- platform: windows, python
- tags: starcraft2, self-play, distributed-training, game-ai, league-training, pytorch, game-development, linux, macos, gpu

## Member repositories
- opendilab/DI-star (main) score 37

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:36.256652+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T04:39:29.931585+00:00, confidence not recorded.
  - readme: https://github.com/opendilab/DI-star (fetched 2026-08-28T04:04:36.256652+00:00, sha 7cd020bee034)
- Data as of 2026-08-30T08:39:29.467469+00:00.