Comments (6)
adamklein
commented on July 19, 2024
Err, I see, since alpha is same across all arms, and sqrt applied as well, doesn't matter. NVM!
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adamklein
commented on July 19, 2024
Actually, spoke to soon. I get a different set of online predictions if alpha and sqrt are used, which makes sense. Is there a theoretical justification for excluding them in this implementation?
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david-cortes
commented on July 19, 2024
Oh yeah I messed that up after the last update, will fix. Thanks for the bug report.
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david-cortes
commented on July 19, 2024
Fixed now, please update to 0.1.8.2.
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david-cortes
commented on July 19, 2024
Actually still not.
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david-cortes
commented on July 19, 2024
Ok, fixed now in 0.1.8.3.
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Related Issues (20)
- Question regarding using contextual bandits for Learning-To-Rank HOT 1
- Different predictions of the same model.dill file in different CPUs for the LinUCB algorithm HOT 1
- Support for continuous rewards HOT 1
- ParametricTS fails with: '_OneVsRest' object has no attribute 'beta_counters' HOT 2
- XGBClassifier becomes un-serializable after being used as a base_model HOT 2
- Need more understanding for the method beta_prior HOT 1
- Any good strategies to run Simulation other then one in Online policy example HOT 1
- How to deal with this Scenario while applying CB techniques HOT 2
- Getting topN arm features in Offpolicy method HOT 4
- Type error if beta_prior == "auto" and nchoices is list HOT 1
- Possibly unexpected behaviour of decision function HOT 1
- _BasePolicy.add_arm(); NameError: name 'base_algorithm' is not defined HOT 1
- AssertionError: online_contextual_bandits.ipynb HOT 1
- TypeError: contextual bandits with custom 'choice_names' (online.py) HOT 3
- Getting topN arm features in onlinepolicy method HOT 9
- topN inputs clarification HOT 3
- warm_start for online policy HOT 1
- Doubt in DREstimator HOT 3
- New release HOT 1
- If r != 0 then could be working for negative reward as well? HOT 1
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