rllib - obtain TensorFlow or PyTorch model output from checkpoint
I'd like to use the rllib trained policy model in a different code where I need to track which action is generated for specific input states. Using a standard TensorFlow or PyTorch (preferred) network model would provide that flexibility but I can't find clear documentation on how to produce a usable dat or H5 file from a trained rllib agent that I can then load into a torch or tf/Keras model.

The easiest way to get the weights from a checkpoint is to load it again with rllib and then save it with the Tensorflow/Pytorch commands.
If you have a keras TF model you can simply call:
model.save('my_model.h5') # creates a HDF5 file

