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Deep Reinforcement Learning training platform

Project description

DRL Wizard

DRL Wizard is a clean, modular Deep Reinforcement Learning toolkit for training, comparing, and understanding modern RL algorithms. It supports multiple environments, multiple algorithms, real-time monitoring, and a unified workflow through a FastAPI backend and Streamlit UI.


🚀 Features

  • Algorithms: PPO, TRPO, DQN, Double DQN, Dueling DQN, SAC (more coming)
  • Environments: Gymnasium, Atari (ALE), image-based & multi-discrete action spaces
  • Architecture: FastAPI backend, Streamlit UI, SQLAlchemy storage, Pydantic configs
  • Experiment Tools: concurrent jobs, graceful stop, NDJSON logs, TensorBoard, job archives
  • Extensible: easy to add algorithms, envs, or visualization components


📦 Installation

Basic

pip install drl-wizard

UI + Dev tools:

pip install drl-wizard[ui,dev]

🖥️ Running

  • Running the UI & Backend:
drl-wizard-run
  • Running Backend:
drl-wizard-api
  • Running UI:
drl-wizard-ui

📝 Notes

  • drl-wizard-run launches the full platform.

  • Extras ([ui], [dev]) include Streamlit, and development dependencies.

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