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Documentation Index

Fetch the complete documentation index at: https://docs.reinforceui-studio.com/llms.txt

Use this file to discover all available pages before exploring further.

Nothing better to explain something than a video. Check out our video tutorial, where we show you how ReinforceUI Studio works

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Run ReinforceUI Studio

Getting started with ReinforceUI Studio is simple and straightforward. After run python main.py Follow these easy steps:
  1. Launch the Application: Upon opening the app, you’ll be greeted with a welcoming window to begin your journey.
  2. Select Your RL Platform: Choose from popular RL platforms like Gymnasium, DeepMind Control Suite, or MuJoCo.
  3. Choose Your Environment: Select the specific environment from a list of available options tailored to your chosen platform.
  4. Pick Your Algorithm: Select the algorithm you want to use for training your model. Choose from a variety of popular algorithms like SAC, TD3, PPO and more.
  5. Configure Training Parameters: Customize your training parameters or use the default settings to get started quickly.
  6. Hit “Start” to begin the training process.
  7. Visualize Progress: Watch real-time training and evaluation curves to track your model’s performance.
And that’s it! You’re ready to train your model.
ReinforceUI Studio is designed primarily for continuous action space environments and algorithms.We now support discrete action space as well, including DQN. However, environments like MuJoCo and DeepMind Control Suite work only with continuous action spaces, so the number of available environments for discrete action space is limited.If you have suggestions for discrete action space environments, let us know—we’d be happy to include them in ReinforceUI Studio!