On saving and loading¶
Stable baselines stores both neural network parameters and algorithm-related parameters such as exploration schedule, number of environments and observation/action space. This allows continual learning and easy use of trained agents without training, but it is not without its issues. Following describes two formats used to save agents in stable baselines, their pros and shortcomings.
Terminology used in this page:
- parameters refer to neural network parameters (also called “weights”). This is a dictionary mapping Tensorflow variable name to a NumPy array.
- data refers to RL algorithm parameters, e.g. learning rate, exploration schedule, action/observation space. These depend on the algorithm used. This is a dictionary mapping classes variable names their values.
Original stable baselines save format. Data and parameters are bundled up into a tuple
and then serialized with
cloudpickle library (essentially the same as
This save format is still available via an argument in model save function in stable-baselines versions above v2.7.0 for backwards compatibility reasons, but its usage is discouraged.
- Easy to implement and use.
- Works with almost any type of Python object, including functions.
- Pickle/Cloudpickle is not designed for long-term storage or sharing between Python version.
- If one object in file is not readable (e.g. wrong library version), then reading the rest of the file is difficult.
- Python-specific format, hard to read stored files from other languages.
If part of a saved model becomes unreadable for any reason (e.g. different Tensorflow versions), then it may be tricky to restore any of the model. For this reason another save format was designed.
A zip-archived JSON dump and NumPy zip archive of the arrays. The data dictionary (class parameters)
is stored as a JSON file, model parameters are serialized with
numpy.savez function and these two files
are stored under a single .zip archive.
Any objects that are not JSON serializable are serialized with cloudpickle and stored as base64-encoded string in the JSON file, along with some information that was stored in the serialization. This allows inspecting stored objects without deserializing the object itself.
This format allows skipping elements in the file, i.e. we can skip deserializing objects that are
broken/non-serializable. This can be done via
custom_objects argument to load functions.
This is the default save format in stable baselines versions after v2.7.0.
saved_model.zip/ ├── data JSON file of class-parameters (dictionary) ├── parameter_list JSON file of model parameters and their ordering (list) ├── parameters Bytes from numpy.savez (a zip file of the numpy arrays). ... ├── ... Being a zip-archive itself, this object can also be opened ... ├── ... as a zip-archive and browsed.
- More robust to unserializable objects (one bad object does not break everything).
- Saved file can be inspected/extracted with zip-archive explorers and by other languages.
- More complex implementation.
- Still relies partly on cloudpickle for complex objects (e.g. custom functions).