Install Asset
Install via Godot
To maintain one source of truth, Godot Asset Library is just a mirror of the old asset library so you can download directly on Godot via the integrated asset library browser
Quick Information
Reinforcement learning for Godot 4.5+ with NATIVE ncnn inference— statically linked C++, no C#/.NET, no external runtime.Train with the standard godot-rl (godot_rl_agents) Python stack; deploy native on web/WASM, console, mobile, desktop, and edge — targets an ONNX/.NET pipeline can't reach.▶ TRY IT IN YOUR BROWSER (no install): https://minigraphx.github.io/godot-native-rl/The demo launcher as a single-threaded WASM build. Trained agents run on native ncnn with no Python at runtime.Pick "Evolution Lab" and the browser tab TRAINS a neural net in front of you.▶ Watch a demo: https://youtu.be/Cud1gvbjg0IWHAT YOU GET• A GDExtension that runs trained policies natively via ncnn — one NcnnRunner node, no runtime dependency.• A godot_rl_agents-compatible training bridge: train with the stock godot-rl Python package, convert to ncnn, deploy.• Declarative reward authoring (Signal→Reward + RewardBuilder) and a sensors library (raycast, relative-position, camera/pixels, grid, navmesh, entity/attention).• In-engine ES training (ESTrainer) — evolve a policy with NO Python, NO socket, NO backprop; runs on every target including web.• Deploy extras a Python-server framework can't match: web/WASM, INT8 quantization, async + batched crowd inference, LOD policy switching, recurrent/LSTM state, continuous + multi-discrete actions.EXAMPLES (all ship a trained net and run standalone — browse them in the launcher above or the examples/ folder)Chase the target • Seek & Avoid • Rover 3D • Ball Chase (SAC) • Fly By (PPO continuous) • Hide & Seek (multi-policy self-play) • Coop Collect (MA-POCA) • Quadruped Walk / Hurdles / Jump • Hexapod • 3DBall & GridWorld (Unity-parity) • Visual Chase (CNN from pixels) • Memory Chase (LSTM) • plus the live Evolution Lab.INSTALLEnable the plugin, then open the demo launcher (F5).Full guide:https://github.com/minigraphx/godot-native-rl/blob/main/docs/guide/getting-started.mdLINKS• Source & README: https://github.com/minigraphx/godot-native-rl• Guides (getting started, running examples, training, deploying, sensors): https://github.com/minigraphx/godot-native-rl/tree/main/docs/guide• ncnn vs ONNX Runtime — honest decision guide: https://github.com/minigraphx/godot-native-rl/blob/main/docs/ncnn_vs_onnx.md• Issues / questions: https://github.com/minigraphx/godot-native-rl/issuesPre-1.0: the API and wire protocol may still change between minor versions.MIT licensed.You don't need a license to run Models in your games.Plugin zip link: https://github.com/minigraphx/godot-native-rl/releases/download/v0.4.0/godot-native-rl-addon-v0.4.0.zipDemos zip link: https://github.com/minigraphx/godot-native-rl/releases/download/v0.4.0/godot-native-rl-examples-v0.4.0.zip
Godot Native RL
Native ncnn inference for Godot 4.5+. Train with
godot_rl_agents, deploy a statically-linked C++ GDExtension (no .NET, no Python runtime) on
desktop, mobile, console, and web. This package bundles the GDScript library and prebuilt
NcnnRunner binaries for every platform.
Quick start
- Enable the plugin: Project → Project Settings → Plugins → Godot Native RL. (Also auto-packs your model files into exports — see below.)
- Add a trained ncnn model (
*.ncnn.param+*.ncnn.bin) to your project. - Drive an agent with
NcnnAIController2D/NcnnAIController3D(control_mode = NCNN_INFERENCE, plus themodel_param_path/model_bin_pathexports), or useNcnnRunnerdirectly:
var runner := NcnnRunner.new()
runner.input_blob_name = "in0"
runner.output_blob_name = "out0"
# Load from bytes so it works inside an exported .pck (incl. web), not just the editor.
runner.load_model_from_buffers(
FileAccess.get_file_as_bytes(param_path),
FileAccess.get_file_as_bytes(bin_path))
var action := runner.run_discrete_action(PackedFloat32Array(obs))
Exporting your game
With the plugin enabled, your *.ncnn.param / *.ncnn.bin are packed into exports
automatically. (Godot otherwise skips these raw data files and the game fails at runtime with
cannot read model files — on every platform.)
Web: in the Web export preset set Extension Support: ON and Thread Support: OFF. The bundled WASM binary is single-threaded, so the game needs no COOP/COEP headers — it runs on itch.io / GitHub Pages with no server configuration.
Full documentation
Converting trained models, training backends, INT8 quantization, sensors, and the API reference: https://github.com/minigraphx/godot-native-rl
Reinforcement learning for Godot 4.5+ with NATIVE ncnn inference
— statically linked C++, no C#/.NET, no external runtime.
Train with the standard godot-rl (godot_rl_agents) Python stack; deploy native on web/WASM, console, mobile, desktop, and edge — targets an ONNX/.NET pipeline can't reach.
▶ TRY IT IN YOUR BROWSER (no install): https://minigraphx.github.io/godot-native-rl/
The demo launcher as a single-threaded WASM build. Trained agents run on native ncnn with no Python at runtime.
Pick "Evolution Lab" and the browser tab TRAINS a neural net in front of you.
▶ Watch a demo: https://youtu.be/Cud1gvbjg0I
WHAT YOU GET
• A GDExtension that runs trained policies natively via ncnn — one NcnnRunner node, no runtime dependency.
• A godot_rl_agents-compatible training bridge: train with the stock godot-rl Python package, convert to ncnn, deploy.
• Declarative reward authoring (Signal→Reward + RewardBuilder) and a sensors library (raycast, relative-position, camera/pixels, grid, navmesh, entity/attention).
• In-engine ES training (ESTrainer) — evolve a policy with NO Python, NO socket, NO backprop; runs on every target including web.
• Deploy extras a Python-server framework can't match: web/WASM, INT8 quantization, async + batched crowd inference, LOD policy switching, recurrent/LSTM state, continuous + multi-discrete actions.
EXAMPLES (all ship a trained net and run standalone — browse them in the launcher above or the examples/ folder)
Chase the target • Seek & Avoid • Rover 3D • Ball Chase (SAC) • Fly By (PPO continuous) • Hide & Seek (multi-policy self-play) • Coop Collect (MA-POCA) • Quadruped Walk / Hurdles / Jump • Hexapod • 3DBall & GridWorld (Unity-parity) • Visual Chase (CNN from pixels) • Memory Chase (LSTM) • plus the live Evolution Lab.
INSTALL
Enable the plugin, then open the demo launcher (F5).
Full guide:
https://github.com/minigraphx/godot-native-rl/blob/main/docs/guide/getting-started.md
LINKS
• Source & README: https://github.com/minigraphx/godot-native-rl
• Guides (getting started, running examples, training, deploying, sensors): https://github.com/minigraphx/godot-native-rl/tree/main/docs/guide
• ncnn vs ONNX Runtime — honest decision guide: https://github.com/minigraphx/godot-native-rl/blob/main/docs/ncnn_vs_onnx.md
• Issues / questions: https://github.com/minigraphx/godot-native-rl/issues
Pre-1.0: the API and wire protocol may still change between minor versions.
MIT licensed.
You don't need a license to run Models in your games.
Plugin zip link: https://github.com/minigraphx/godot-native-rl/releases/download/v0.4.0/godot-native-rl-addon-v0.4.0.zip
Demos zip link: https://github.com/minigraphx/godot-native-rl/releases/download/v0.4.0/godot-native-rl-examples-v0.4.0.zip
Reviews
Quick Information
Reinforcement learning for Godot 4.5+ with NATIVE ncnn inference— statically linked C++, no C#/.NET, no external runtime.Train with the standard godot-rl (godot_rl_agents) Python stack; deploy native on web/WASM, console, mobile, desktop, and edge — targets an ONNX/.NET pipeline can't reach.▶ TRY IT IN YOUR BROWSER (no install): https://minigraphx.github.io/godot-native-rl/The demo launcher as a single-threaded WASM build. Trained agents run on native ncnn with no Python at runtime.Pick "Evolution Lab" and the browser tab TRAINS a neural net in front of you.▶ Watch a demo: https://youtu.be/Cud1gvbjg0IWHAT YOU GET• A GDExtension that runs trained policies natively via ncnn — one NcnnRunner node, no runtime dependency.• A godot_rl_agents-compatible training bridge: train with the stock godot-rl Python package, convert to ncnn, deploy.• Declarative reward authoring (Signal→Reward + RewardBuilder) and a sensors library (raycast, relative-position, camera/pixels, grid, navmesh, entity/attention).• In-engine ES training (ESTrainer) — evolve a policy with NO Python, NO socket, NO backprop; runs on every target including web.• Deploy extras a Python-server framework can't match: web/WASM, INT8 quantization, async + batched crowd inference, LOD policy switching, recurrent/LSTM state, continuous + multi-discrete actions.EXAMPLES (all ship a trained net and run standalone — browse them in the launcher above or the examples/ folder)Chase the target • Seek & Avoid • Rover 3D • Ball Chase (SAC) • Fly By (PPO continuous) • Hide & Seek (multi-policy self-play) • Coop Collect (MA-POCA) • Quadruped Walk / Hurdles / Jump • Hexapod • 3DBall & GridWorld (Unity-parity) • Visual Chase (CNN from pixels) • Memory Chase (LSTM) • plus the live Evolution Lab.INSTALLEnable the plugin, then open the demo launcher (F5).Full guide:https://github.com/minigraphx/godot-native-rl/blob/main/docs/guide/getting-started.mdLINKS• Source & README: https://github.com/minigraphx/godot-native-rl• Guides (getting started, running examples, training, deploying, sensors): https://github.com/minigraphx/godot-native-rl/tree/main/docs/guide• ncnn vs ONNX Runtime — honest decision guide: https://github.com/minigraphx/godot-native-rl/blob/main/docs/ncnn_vs_onnx.md• Issues / questions: https://github.com/minigraphx/godot-native-rl/issuesPre-1.0: the API and wire protocol may still change between minor versions.MIT licensed.You don't need a license to run Models in your games.Plugin zip link: https://github.com/minigraphx/godot-native-rl/releases/download/v0.4.0/godot-native-rl-addon-v0.4.0.zipDemos zip link: https://github.com/minigraphx/godot-native-rl/releases/download/v0.4.0/godot-native-rl-examples-v0.4.0.zip