[Enhancement] Support for fallbacks when loading cloudpickle items fail

Related issue #171

As seen in #171, cloudpickle/pickle can easily fail when transferring models between Python versions (and in case some other shenanigans). There is currently no way to address this issue as loading of model tries to un-pickle all pickle objects and does not catch errors.

Two suggestions to deal with this, both of which would be nice to have:

1. Provide custom_objects dictionary (similar to what we had in SB2), which will override any objects read from file. This could be used to even prevent loading the item from the file, in which case all JSON-ed items can be used as before.
2. Update set_parameters function to only load parameters from the file but not the data objects (which include pickled items). This was included in SB2.

@ManifoldFR If you had other suggestions please feel free to throw them here 😃

[ManifoldFR]

From what I saw the problem I encountered is with the learning rate and clip ratio schedulers (for PPO), in the particular case where a constant scheduler is created using constant_fn (I don’t know about other cases though) because pickling/unpickling functions is brittle especially when they are locals of a higher-order functon. I think we can look at how PyTorch supports saving state dicts for schedulers. From the source I see they use classes and the __dict__ attribute to save/load state dicts.

In general I think the ideas for other fallbacks are good because it allows the user more flexibility.

[araffin]

as a follow-up, I added support for custom objects here: https://github.com/DLR-RM/stable-baselines3/pull/336 and included that in the rl zoo: https://github.com/DLR-RM/rl-baselines3-zoo/pull/69 (so models trained with python 3.6/3.7 can be loaded with python 3.8+, but not retrained yet)