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Comprehensive machine learning prototyping Tools for Every Need

Get access to machine learning prototyping solutions that address multiple requirements. One-stop resources for streamlined workflows.

machine learning prototyping

  • Vanilla Agents

    Vanilla Agents provides ready-to-use implementations of DQN, PPO, and A2C RL agents with customizable training pipelines.
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    What is Vanilla Agents?
    Vanilla Agents is a lightweight PyTorch-based framework that delivers modular and extensible implementations of core reinforcement learning agents. It supports algorithms like DQN, Double DQN, PPO, and A2C, with pluggable environment wrappers compatible with OpenAI Gym. Users can configure hyperparameters, log training metrics, save checkpoints, and visualize learning curves. The codebase is organized for clarity, making it ideal for research prototyping, educational use, and benchmarking new ideas in RL.
    Vanilla Agents Core Features
    • DQN and Double DQN implementations
    • PPO and A2C policy-gradient agents
    • OpenAI Gym environment wrappers
    • Configurable hyperparameters
    • Logging and TensorBoard support
    • Model checkpoint saving and loading

  • mini-AlphaStar

    Simplified PyTorch implementation of AlphaStar, enabling StarCraft II RL agent training with modular network architecture and self-play.
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    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.

  • Scalable MADDPG

    Scalable MADDPG is an open-source multi-agent reinforcement learning framework implementing deep deterministic policy gradient for multiple agents.
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    What is Scalable MADDPG?
    Scalable MADDPG is a research-oriented framework for multi-agent reinforcement learning, offering a scalable implementation of the MADDPG algorithm. It features centralized critics during training and independent actors at runtime for stability and efficiency. The library includes Python scripts to define custom environments, configure network architectures, and adjust hyperparameters. Users can train multiple agents in parallel, monitor metrics, and visualize learning curves. It integrates with OpenAI Gym-like environments and supports GPU acceleration via TensorFlow. By providing modular components, Scalable MADDPG enables flexible experimentation on cooperative, competitive, or mixed multi-agent tasks, facilitating rapid prototyping and benchmarking.
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