Question: How can I manage the reproducibility of an experiment? about mushroom-rl HOT 5 CLOSED

Another thing, I just notice that parameter value is updated on each step.
Is there anyway to do this when the episode ends?

I currently was to do a class and call it in a step callback.

class EpisodicDecay:
    def __init__(self, parameter):
        self.parameter = parameter
        self._init_table = parameter._n_updates.table.copy()
        
    def __call__(self, dataset):
        if dataset[-1][-1]: # episode has ended
            self._init_table += 1
        self.parameter._n_updates.table = self._init_table.copy()

from mushroom-rl.

You need also to set the numpy seed, as it influences the policy.
If you add torch, you should also set that seed too.

In general, we cannot write a general method to set the seed, as many libraries could use different random generators.
e.g. the enviroment seed method is only for gym enviroments, all the others use default numpy seed.

For the parameter decay, the one that you propose is the only supported way to achieve that behavior. That's exactly one of the use cases of callbacks.

from mushroom-rl.

ooh right!
Yes I know how do that, and make sense. Thanks!

And about the episodic decay parameter, that's ok, I can handle that.

Thanks!

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I get NotImplementedError when I try to set seed on some of the envrionments. Any thoughts on how to fix it?

env = PuddleWorld()
env.seed(seed)

 File  "/python3.8/site-packages/mushroom_rl/core/environment.py", line 137, in seed
     raise NotImplementedError
 NotImplementedError

from mushroom-rl.

see my answer to #78

from mushroom-rl.