Add tensorflow.js export example

CONTRIBUTING.md

@@ -33,7 +33,7 @@ cd stable-baselines3/
 2. Install Stable-Baselines3 in develop mode, with support for building the docs and running tests:
 
 ```bash
-pip install -e .[docs,tests,extra]
+pip install -e '.[docs,tests,extra]'
 ```
 
 ## Codestyle

README.md

@@ -210,7 +210,7 @@ Actions `gymnasium.spaces`:
 ## Testing the installation
 ### Install dependencies
 ```sh
-pip install -e .[docs,tests,extra]
+pip install -e '.[docs,tests,extra]'
 ```
 ### Run tests
 All unit tests in stable baselines3 can be run using `pytest` runner:

docs/guide/export.rst

@@ -37,8 +37,7 @@ If you are using PyTorch 2.0+ and ONNX Opset 14+, you can easily export SB3 poli
 
 .. warning::
 
-  The following returns normalized actions and doesn't include the `post-processing <https://github.com/DLR-RM/stable-baselines3/blob/a9273f968eaf8c6e04302a07d803eebfca6e7e86/stable_baselines3/common/policies.py#L370-L377>`_ step that is done with continuous actions
-  (clip or unscale the action to the correct space).
+  The following returns normalized actions and doesn't include the `post-processing <https://github.com/DLR-RM/stable-baselines3/blob/a9273f968eaf8c6e04302a07d803eebfca6e7e86/stable_baselines3/common/policies.py#L370-L377>`_ step that is done with continuous actions (clip or unscale the action to the correct space).
 
 
 .. code-block:: python
@@ -195,10 +194,22 @@ There is a draft PR in the RL Zoo about C++ export: https://github.com/DLR-RM/rl
 Export to ONNX-JS / ONNX Runtime Web
 ------------------------------------
 
-See https://onnxruntime.ai/docs/tutorials/web/build-web-app.html
+Official documentation: https://onnxruntime.ai/docs/tutorials/web/build-web-app.html
+
+Full example code: https://github.com/JonathanColetti/CarDodgingGym
+
+Demo: https://jonathancoletti.github.io/CarDodgingGym
+
+The code linked above is a complete example (using car dodging environment) that:
+
+1. Creates/Trains a PPO model
+2. Exports the model to ONNX along with normalization stats in JSON
+3. Runs in the browser with normalization using onnxruntime-web to achieve similar results
+
+Below is a simple example with converting to ONNX then inferencing without postprocess in ONNX-JS
 
 .. code-block:: python
-  
+
   import torch as th
 
   from stable_baselines3 import SAC
@@ -210,8 +221,7 @@ See https://onnxruntime.ai/docs/tutorials/web/build-web-app.html
           self.actor = actor
 
       def forward(self, observation: th.Tensor) -> th.Tensor:
-          # NOTE: You may have to postprocess (unnormalize) actions
-          # to the correct bounds (see commented code below)
+          # NOTE: You may have to postprocess (unnormalize or renormalize)
           return self.actor(observation, deterministic=True)
 
 
@@ -231,8 +241,8 @@ See https://onnxruntime.ai/docs/tutorials/web/build-web-app.html
   )
 
 .. code-block:: javascript
-  
-  // Install using `npm install onnxruntime-web` or using cdn
+
+  // Install using `npm install onnxruntime-web` (tested with version 1.19) or using cdn
   import * as ort from 'onnxruntime-web';
 
   async function runInference() {
@@ -254,11 +264,86 @@ See https://onnxruntime.ai/docs/tutorials/web/build-web-app.html
   runInference();
 
 
-Export to tensorflowjs 
-----------------------
+Export to TensorFlow.js
+-----------------------
+
+.. warning::
+
+  As of November 2025, `onnx2tf <https://github.com/PINTO0309/onnx2tf>`_ does not support TensorFlow.js. Therefore, `tfjs-converter <https://github.com/tensorflow/tfjs-converter>`_ is used instead. However, tfjs-converter is not currently maintained and requires older opsets and TensorFlow versions.
+
+
+In order for this to work, you have to do multiple conversions: SB3 => ONNX => TensorFlow => TensorFlow.js.
+
+The opset version needs to be changed for the conversion (``opset_version=14`` is currently required). Please refer to the code above for more stable usage with a higher opset.
+
+The following is a simple example that showcases the full conversion + inference.
+
+Please refer to the previous sections for the first step (SB3 => ONNX).
+The main difference is that you need to specify ``opset_version=14``.
+
+.. code-block:: python
+
+  # Tested with python3.10
+  # Then install these dependencies in a fresh env
+  """
+  pip install --use-deprecated=legacy-resolver tensorflow==2.13.0 keras==2.13.1 onnx==1.16.0 onnx-tf==1.9.0 tensorflow-probability==0.21.0 tensorflowjs==4.15.0 jax==0.4.26 jaxlib==0.4.26
+  """
+  # Then run this codeblock
+  # If there are no errors (the folder is structure correctly) then
+  """
+  # tensorflowjs_converter --input_format=tf_saved_model --output_format=tfjs_graph_model tf_model tfjs_model
+  """
+
+  # If you get an error exporting using `tensorflowjs_converter` then upgrade tensorflow
+  """
+  pip install --upgrade tensorflow tensorflow-decision-forests tensorflowjs
+  """
+  # And retry with and it should work (do not rerun this codeblock)
+  """
+  tensorflowjs_converter --input_format=tf_saved_model --output_format=tfjs_graph_model tf_model tfjs_model
+  """
 
-TODO: contributors help is welcomed!
-Probably a good starting point: https://github.com/elliotwaite/pytorch-to-javascript-with-onnx-js
+  import onnx
+  import onnx_tf.backend
+  import tensorflow as tf
+
+  ONNX_FILE_PATH = "my_sac_actor.onnx"
+  MODEL_PATH = "tf_model"
+
+  onnx_model = onnx.load(ONNX_FILE_PATH)
+  onnx.checker.check_model(onnx_model)
+  print(onnx.helper.printable_graph(onnx_model.graph))
+
+  print('Converting ONNX to TF...')
+  tf_rep = onnx_tf.backend.prepare(onnx_model)
+  tf_rep.export_graph(MODEL_PATH)
+  # After this do not forget to use `tensorflowjs_converter`
+
+
+.. code-block:: javascript
+
+  import * as tf from 'https://cdn.jsdelivr.net/npm/@tensorflow/[email protected]/+esm';
+  // Post processing not included
+  async function runInference() {
+      const MODEL_URL = './tfjs_model/model.json';
+
+      const model = await tf.loadGraphModel(MODEL_URL);
+
+      // Observation_size is 3 for Pendulum-v1
+      const inputData = [1.0, 0.0, 0.0];
+      const inputTensor = tf.tensor2d([inputData], [1, 3]);
+
+      const resultTensor = model.execute(inputTensor);
+
+      const action = await resultTensor.data();
+
+      console.log('Predicted action=', action);
+
+      inputTensor.dispose();
+      resultTensor.dispose();
+  }
+
+  runInference();
 
 
 Export to TFLite / Coral (Edge TPU)

docs/misc/changelog.rst

@@ -44,6 +44,9 @@ Documentation:
 - Added sb3-plus to projects page
 - Added example usage of ONNX JS
 - Updated link to paper of community project DeepNetSlice (@AlexPasqua)
+- Added example usage of Tensorflow JS
+- Included exact versions in ONNX JS and example project
+- Made step 2 (`pip install`) of `CONTRIBUTING.md` more robust 
 
 
 Release 2.7.0 (2025-07-25)