mini-AlphaStar is a minimal PyTorch-based reproduction of DeepMind's AlphaStar, providing researchers and enthusiasts with an easy-to-follow framework for building RL agents in StarCraft II. It integrates key components like spatial and non-spatial encoders, LSTM-based memory, policy and value heads, self-play training loops, and environment wrappers. The project includes scripts for data preparation, model training, evaluation, and TensorBoard logging.
AI Platforms & Frameworks
Education
Gaming
#StarCraft II AI
#Open Source Framework
#PyTorch AI development
#reinforcement learning
#imitation learning
#multi-agent systems
#LSTM networks
#game AI training
#TensorBoard visualization
#educational AI tools
#dataset generation
#action selection models
#value networks
#modular codebase
#StarCraft environment wrappers
#self-play training
#hyperparameter tuning
#machine learning prototyping
#AI research tool
#Game Development
...
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mini-AlphaStar
mini-AlphaStar is a minimal PyTorch-based reproduction of DeepMind's AlphaStar, providing researchers and enthusiasts with an easy-to-follow framework for building RL agents in StarCraft II. It integrates key components like spatial and non-spatial encoders, LSTM-based memory, policy and value heads, self-play training loops, and environment wrappers. The project includes scripts for data preparation, model training, evaluation, and TensorBoard logging.
AI Platforms & Frameworks
Education
Gaming
#StarCraft II AI
#Open Source Framework
#PyTorch AI development
#reinforcement learning
#imitation learning
#multi-agent systems
#LSTM networks
#game AI training
#TensorBoard visualization
#educational AI tools
#dataset generation
#action selection models
#value networks
#modular codebase
#StarCraft environment wrappers
#self-play training
#hyperparameter tuning
#machine learning prototyping
#AI research tool
#Game Development
...
Featured
What is mini-AlphaStar?
mini-AlphaStar demystifies the complex AlphaStar architecture by offering an accessible, open-source PyTorch framework for StarCraft II AI development. It features spatial feature encoders for screen and minimap inputs, non-spatial feature processing, LSTM memory modules, and separate policy and value networks for action selection and state evaluation. Using imitation learning to bootstrap and reinforcement learning with self-play for fine-tuning, it supports environment wrappers compatible with StarCraft II via pysc2, logging through TensorBoard, and configurable hyperparameters. Researchers and students can generate datasets from human gameplay, train models on custom scenarios, evaluate agent performance, and visualize learning curves. The modular codebase enables easy experimentation with network variants, training schedules, and multi-agent setups. Designed for education and prototyping rather than production deployment.
Who will use mini-AlphaStar?
- AI researchers
- Reinforcement learning practitioners
- Game AI developers
- Students and educators
- Machine learning enthusiasts
How to use the mini-AlphaStar?
- Step1: Clone the repository from GitHub.
- Step2: Install Python 3.7+ and required dependencies via pip.
- Step3: Install and configure StarCraft II and the pysc2 environment.
- Step4: Run data preparation scripts to collect or import gameplay datasets.
- Step5: Execute the imitation learning training script to initialize the policy.
- Step6: Launch the reinforcement learning self-play script to fine-tune the agent.
- Step7: Monitor training progress and metrics with TensorBoard.
- Step8: Run evaluation scripts to assess agent performance on defined scenarios.
Platform
- mac
- windows
- linux
mini-AlphaStar's Core Features & Benefits
The Core Features
- Spatial and non-spatial feature encoding
- LSTM-based memory modules
- Separate policy and value networks
- Imitation learning and reinforcement learning pipelines
- Self-play environment wrappers via pysc2
- TensorBoard logging and visualization
- Configurable hyperparameters
- Modular PyTorch codebase
The Benefits
- Educational and easy-to-understand implementation
- Open-source and customizable
- Reproducible StarCraft II RL experiments
- Modular architecture for rapid prototyping
- Integration with standard ML tools
- Supports multi-agent self-play
mini-AlphaStar's Main Use Cases & Applications
- Teaching reinforcement learning concepts with a real-time strategy game
- Prototyping custom StarCraft II AI agents
- Researching network architectures for game-playing agents
- Benchmarking imitation learning vs. self-play performance
- Visualizing RL training dynamics and reward curves
FAQs of mini-AlphaStar
mini-AlphaStar Company Information
mini-AlphaStar Reviews
5/5
mini-AlphaStar's Main Competitors and alternatives?
- DeepMind AlphaStar (closed implementation)
- SC2LE / pysc2
- SMAC (StarCraft Multi-Agent Challenge)
- OpenAI Gym (general RL benchmarks)
- Dopamine (RL framework)
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