Exporting models

After training an agent, you may want to deploy/use it in another language or framework, like tensorflowjs. Stable Baselines3 does not include tools to export models to other frameworks, but this document aims to cover parts that are required for exporting along with more detailed stories from users of Stable Baselines3.


In Stable Baselines3, the controller is stored inside policies which convert observations into actions. Each learning algorithm (e.g. DQN, A2C, SAC) contains a policy object which represents the currently learned behavior, accessible via model.policy.

Policies hold enough information to do the inference (i.e. predict actions), so it is enough to export these policies (cf examples) to do inference in another framework.


When using CNN policies, the observation is normalized during pre-preprocessing. This pre-processing is done inside the policy (dividing by 255 to have values in [0, 1])

Export to ONNX

TODO: help is welcomed!

Export to C++

(using PyTorch JIT) TODO: help is welcomed!

Export to tensorflowjs / ONNX-JS

TODO: contributors help is welcomed! Probably a good starting point: https://github.com/elliotwaite/pytorch-to-javascript-with-onnx-js

Manual export

You can also manually export required parameters (weights) and construct the network in your desired framework.

You can access parameters of the model via agents’ get_parameters function. As policies are also PyTorch modules, you can also access model.policy.state_dict() directly. To find the architecture of the networks for each algorithm, best is to check the policies.py file located in their respective folders.


In most cases, we recommend using PyTorch methods state_dict() and load_state_dict() from the policy, unless you need to access the optimizers’ state dict too. In that case, you need to call get_parameters().