reinforceui-studio 1.3.1

A GUI to simplify the configuration and monitoring of RL training processes.

🚀 ReinforceUI Studio: Reinforcement Learning Made Simple

Intuitive, Powerful, and Hassle-Free RL Training & Monitoring – All in One Place.

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⭐️ If you find this project useful, please consider giving it a star! It really helps!

📚 Full Documentation: https://docs.reinforceui-studio.com

🎬 Video Demo: YouTube Tutorial

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What is ReinforceUI Studio?

ReinforceUI Studio is a Python-based application designed to simplify Reinforcement Learning (RL) workflows through a beautiful, intuitive GUI. No more memorizing commands, no more juggling extra repos – just train, monitor, and evaluate in a few clicks!

Quickstart

Getting started with ReinforceUI Studio is fast and easy!

🖥️ Install and Run Locally

The recommended way to use ReinforceUI Studio is by running it locally. This gives you full control over the application, with no extra tools needed.

Follow these simple steps:

1. Clone the repository and install dependencies

git clone https://github.com/dvalenciar/ReinforceUI-Studio.git
cd ReinforceUI-Studio
pip install -r requirements.txt

2. Run the application

python main.py 

That's it! You’re ready to start training and monitoring your Reinforcement Learning agents through an intuitive GUI.

✅ Tip: If you encounter any issues, check out the Installation Guide in the full documentation.

Why you should use ReinforceUI Studio

• 🚀 Instant RL Training: Configure environments, select algorithms, set hyperparameters – all in seconds.
• 🖥️ Real-Time Dashboard: Watch your agents learn with live performance curves and metrics.
• 🧠 Multi-Algorithm Support: Train and compare multiple algorithms simultaneously.
• 📦 Full Logging: Automatically save models, plots, evaluations, videos, and training stats.
• 🔧 Easy Customization: Adjust hyperparameters or load optimized defaults.
• 🧩 Environment Support: Works with MuJoCo, OpenAI Gymnasium, and DeepMind Control Suite.
• 📊 Final Comparison Plots: Auto-generate publishable comparison graphs for your reports or papers.

Quick Overview: Single and Multi-Algorithm Training

• Single Training: Choose an algorithm, tweak parameters, train & visualize.

• Multi-Training: Select several algorithms, run them simultaneously, and compare performances side-by-side.

Selection Window	Main Window Display

Supported Algorithms

ReinforceUI Studio supports the following algorithms:

Algorithm	Description
CTD4	Continuous Distributional Actor-Critic Agent with a Kalman Fusion of Multiple Critics
DDPG	Deep Deterministic Policy Gradient
DQN	Deep Q-Network
PPO	Proximal Policy Optimization
SAC	Soft Actor-Critic
TD3	Twin Delayed Deep Deterministic Policy Gradient
TQC	Controlling Overestimation Bias with Truncated Mixture of Continuous Distributional Quantile Critics

Results Examples

Below are some examples of results generated by ReinforceUI Studio, showcasing the evaluation curves along with snapshots of the policies in action.

Algorithm	Platform	Environment	Curve	Video
SAC	DMCS	Walker Walk
TD3	MuJoCo	HalfCheetah v5
CDT4	DMCS	Ball in cup catch
DQN	Gymnasium	CartPole v1

Citation

If you find ReinforceUI Studio useful for your research or project, please kindly star this repo and cite is as follows:

@misc{reinforce_ui_studio_2025,
  title = { ReinforceUI Studio: Simplifying Reinforcement Learning Training and Monitoring},
  author = {David Valencia Redrovan},
  year = {2025},
  publisher = {GitHub},
  url = {https://github.com/dvalenciar/ReinforceUI-Studio.}
}

Why Star ⭐ this Repository?

Your support helps the project grow! If you like ReinforceUI Studio, please star ⭐ this repository and share it with friends, colleagues, and the RL community! Together, we can make Reinforcement Learning accessible to everyone!

License

ReinforceUI Studio is licensed under the MIT License. You are free to use, modify, and distribute this software, provided that the original copyright notice and license are included in any copies or substantial portions of the software.

Acknowledgements

This project was inspired by the CARES Reinforcement Learning Package from the University of Auckland