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

If you are using PyTorch 2.0+ and ONNX Opset 14+, you can easily export SB3 policies using the following code:


The following returns normalized actions and doesn’t include the post-processing step that is done with continuous actions (clip or unscale the action to the correct space).

import torch as th
from typing import Tuple

from stable_baselines3 import PPO
from stable_baselines3.common.policies import BasePolicy

class OnnxableSB3Policy(th.nn.Module):
    def __init__(self, policy: BasePolicy):
        self.policy = policy

    def forward(self, observation: th.Tensor) -> Tuple[th.Tensor, th.Tensor, th.Tensor]:
        # NOTE: Preprocessing is included, but postprocessing
        # (clipping/inscaling actions) is not,
        # If needed, you also need to transpose the images so that they are channel first
        # use deterministic=False if you want to export the stochastic policy
        # policy() returns `actions, values, log_prob` for PPO
        return self.policy(observation, deterministic=True)

# Example: model = PPO("MlpPolicy", "Pendulum-v1")
PPO("MlpPolicy", "Pendulum-v1").save("PathToTrainedModel")
model = PPO.load("PathToTrainedModel.zip", device="cpu")

onnx_policy = OnnxableSB3Policy(model.policy)

observation_size = model.observation_space.shape
dummy_input = th.randn(1, *observation_size)

##### Load and test with onnx

import onnx
import onnxruntime as ort
import numpy as np

onnx_path = "my_ppo_model.onnx"
onnx_model = onnx.load(onnx_path)

observation = np.zeros((1, *observation_size)).astype(np.float32)
ort_sess = ort.InferenceSession(onnx_path)
actions, values, log_prob = ort_sess.run(None, {"input": observation})

print(actions, values, log_prob)

# Check that the predictions are the same
with th.no_grad():
    print(model.policy(th.as_tensor(observation), deterministic=True))

For SAC the procedure is similar. The example shown only exports the actor network as the actor is sufficient to roll out the trained policies.

import torch as th

from stable_baselines3 import SAC

class OnnxablePolicy(th.nn.Module):
    def __init__(self, actor: th.nn.Module):
        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)
        return self.actor(observation, deterministic=True)

# Example: model = SAC("MlpPolicy", "Pendulum-v1")
SAC("MlpPolicy", "Pendulum-v1").save("PathToTrainedModel.zip")
model = SAC.load("PathToTrainedModel.zip", device="cpu")
onnxable_model = OnnxablePolicy(model.policy.actor)

observation_size = model.observation_space.shape
dummy_input = th.randn(1, *observation_size)

##### Load and test with onnx

import onnxruntime as ort
import numpy as np

onnx_path = "my_sac_actor.onnx"

observation = np.zeros((1, *observation_size)).astype(np.float32)
ort_sess = ort.InferenceSession(onnx_path)
scaled_action = ort_sess.run(None, {"input": observation})[0]


# Post-process: rescale to correct space
# Rescale the action from [-1, 1] to [low, high]
# low, high = model.action_space.low, model.action_space.high
# post_processed_action = low + (0.5 * (scaled_action + 1.0) * (high - low))

# Check that the predictions are the same
with th.no_grad():
    print(model.actor(th.as_tensor(observation), deterministic=True))

For more discussion around the topic, please refer to GH#383 and GH#1349.

Trace/Export to C++

You can use PyTorch JIT to trace and save a trained model that can be re-used in other applications (for instance inference code written in C++).

There is a draft PR in the RL Zoo about C++ export: https://github.com/DLR-RM/rl-baselines3-zoo/pull/228

# See "ONNX export" for imports and OnnxablePolicy
jit_path = "sac_traced.pt"

# Trace and optimize the module
traced_module = th.jit.trace(onnxable_model.eval(), dummy_input)
frozen_module = th.jit.freeze(traced_module)
frozen_module = th.jit.optimize_for_inference(frozen_module)
th.jit.save(frozen_module, jit_path)

##### Load and test with torch

import torch as th

dummy_input = th.randn(1, *observation_size)
loaded_module = th.jit.load(jit_path)
action_jit = loaded_module(dummy_input)

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

Export to TFLite / Coral (Edge TPU)

Full example code: https://github.com/chunky/sb3_to_coral

Google created a chip called the “Coral” for deploying AI to the edge. It’s available in a variety of form factors, including USB (using the Coral on a Raspberry Pi, with a SB3-developed model, was the original motivation for the code example above).

The Coral chip is fast, with very low power consumption, but only has limited on-device training abilities. More information is on the webpage here: https://coral.ai.

To deploy to a Coral, one must work via TFLite, and quantize the network to reflect the Coral’s capabilities. The full chain to go from SB3 to Coral is: SB3 (Torch) => ONNX => TensorFlow => TFLite => Coral.

The code linked above is a complete, minimal, example that:

  1. Creates a model using SB3

  2. Follows the path of exports all the way to TFLite and Google Coral

  3. Demonstrates the forward pass for most exported variants

There are a number of pitfalls along the way to the complete conversion that this example covers, including:

  • Making the Gym’s observation work with ONNX properly

  • Quantising the TFLite model appropriately to align with Gym while still taking advantage of Coral

  • Using OnnxablePolicy described as described in the above example

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().