ExploreRL
A SwiftUI app to help learn about and understand reinforcement learning. Experiment with environments, visualize learning signals, and explore different algorithms. ExploreRL runs on Gymnazo, a native Swift library that brings Gymnasium-style environments and RL utilities to Apple Silicon devices with MLX.
Interactive Training
Train agents on device using robust algorithm implementations. Adjust hyperparameters like learning rate, gamma, epsilon, and batch sizes to see how they directly affect learning performance.
Live Visualization
Watch your agent learn in real-time. ExploreRL can render the environment state alongside live charts displaying episode rewards, value estimates, and policy metrics.
Save Session
Stop training at any time. Saved sessions archive the environment ID, algorithm state, hyperparameters, and checkpoints (network weights/Q-tables). Export and share them anywhere via .xrlsession files.
Explore
A dedicated in-app educational Hub breaking down the math and logic behind the RL concepts used within the app.
Support & Feedback
Have questions, found a bug, or want to request a new environment or algorithm?
Supported Devices
ExploreRL leverages MLX Swift. It currently requires an Apple Silicon device running macOS 15+ or iOS 18+. Devices with newer hardware and more memory will have an easier time training more demanding environments and algorithms.
Tested on MacBook Pro M4 Pro, iPad Pro (M2), and iPhone 17 Pro (A19 Pro).