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

ReinforceUI Studio Initial Tutorial

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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 Reinforcement Learning platforms like Gymnasium, DeepMind Control Suite (DMCS), 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, TQC 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 to for environments and algorithms for CONTINUOUS ACTION SPACE only.

Which this means? So, it is not suitable for environments such as Atari games or discrete control tasks and algorithms like DQN.

Why do we have this limitation? Because these days robotics and many other real-world applications are using continuous action space environments and algorithms. So, we keep our focus on this area.

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