Can I import my own deep reinforcement learning algorithm based on PyTorch, which is written in Python, into MATLAB/Simulink?

By checking the importNetworkFromPyTorch documentation, it appears that the provided models have predefined architectures with limited flexibility. Can I still import my custom deep reinforcement learning algorithm, developed in PyTorch, using this function or another method?

Answer 1

[Krishna]

Hello yfxcore,

Should you encounter difficulties with the ‘importNetworkFromPyTorch’ function, there are two alternative strategies you might consider:

Initially, attempt to save your PyTorch model's weights into a file, preferably using the HDF5 format. Subsequently, these weights can be integrated into a MATLAB model featuring a similar structure. This process will involve the recreation of the PyTorch model's design in MATLAB code and the careful alignment of weight matrices. Begin by storing the PyTorch model's weights in an HDF5 file via Python. Then, in MATLAB, load these weights and map them to the respective layers within your MATLAB model.

Alternatively, PyTorch models can be transformed into the ONNX (Open Neural Network Exchange) format, which is a universally recognized standard for machine learning models. MATLAB is equipped with the capability to import ONNX models. Therefore, you can convert your trained PyTorch model into ONNX format and subsequently import it to MATLAB for inference. Here are the general steps to follow:

• Convert your PyTorch model into ONNX format within PyTorch.
• Utilize MATLAB's ‘importNetworkFromONNX’ function to import the ONNX model.
• Deploy the imported model for inference tasks within MATLAB.

For additional guidance on this process, please consult the following resource:

https://in.mathworks.com/help/deeplearning/ref/importnetworkfromonnx.html

Also you can refer to this MATLAB answer as well it might help your problem, https://in.mathworks.com/matlabcentral/answers/607941-how-to-load-a-fully-connected-pytorch-model-trained-model-into-matlab

Hope this helps.