Comments (2)
Do you refer to a discounting factor as used in non-stationary bandit problems? contextual
currently focuses on stationary bandit problems - but should not be hard to implement both non-stationary type bandits and discounting policies. See for instance https://github.com/Nth-iteration-labs/contextual/blob/master/demo/demo_subsubclass.R for an annealing bandit policy to get you started.
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I will close the issue due to inactivity for now. Feel free to reopen the issue if and when you consider this question still unanswered, though!
from contextual.
Related Issues (20)
- Save predicted reward for chosen arm (feature request) HOT 2
- Some minor clarifications in the documentation HOT 1
- Help with creating a custom bandit. Error message: cannot add bindings to a locked environment HOT 3
- Minor update required for EpsilonFirstPolicy object input in documentation article HOT 1
- CRAN packages' problems on R-devel: `[[<-`(NULL, *)
- cannot have more than two simulations per epoch using benchmark MAB policy in offline bandit CMAB policy evaluation HOT 4
- contextual do_parallel dosnt work on MRAN 3.5.1 HOT 3
- Saved the trained agent and hold the thetas unchanged for simulation on new dataset
- Contextual, determinism and setting seeds HOT 1
- Arm choice sequence from the simulation? HOT 1
- minor correction for statement in demo "Demo: Bandits, Propensity Weighting & Simpson’s Paradox in R" HOT 2
- Confusing assignments in method get_reward for OfflineDoublyRobustBandit HOT 2
- Possible bug in Exp3Policy HOT 3
- How to get predictions of new test data?
- Package removed from CRAN
- Example in ContextualBinaryBandit doc does not run HOT 2
- abline() does not draw lines where expected HOT 2
- Typo in documentation for ContextualEpochGreedyPolicy HOT 2
- Setting random seed outside of Simulator HOT 2
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