default p_min

[ianlini]

https://github.com/ntucllab/striatum/blob/master/striatum/bandit/exp4p.py#L64
What's this? Any reference?

[yangarbiter]

It should be this one
https://github.com/ntucllab/libact/blob/master/libact/query_strategies/active_learning_by_learning.py#L337

[ianlini]

How do we define T?

[taweihuang]

The problem here is that we do not know the exact value of N and T for initialization.
T is the total times of recommendation, while N is the number of experts.

[ianlini]

I think we should fix N.
What happens if we have more than T rounds?

[ianlini]

I think the actions and experts should both be fixed...
I don't think Exp4.P can handle changes of actions and experts reasonably...
This is a big change, any idea?
@yangarbiter @stegben @SoluMilken

[taweihuang]

Yeah, the original EXP4P cannot handle new actions and experts.
For new actions, if we retrain our experts, I think it's still okay. But for new experts, I think the original algorithm could not handle this case.

[ianlini]

After retraining the experts, I don't think the weight can still work, and the new weight of a new action is also a problem.