BanditEpsilonLearner

class coba.learners.BanditEpsilonLearner

Select the greedy action with probability (1-epsilon).

Constructors

__init__(epsilon: float = 0.05, seed: int = 1) → None

Instantiate an BanditEpsilonLearner.

Parameters:

  • epsilon – We explore with probability epsilon and exploit otherwise.

  • seed – The seed used to select actions in predict.

Methods

learn(context: Context, action: Action, reward: float, probability: float) → None

Learn about the action taken in the context.

Parameters:

  • context – The context in which the action was taken.

  • action – The action that was taken.

  • reward – The reward for the given context and action (feedback for IGL problems).

  • probability – The probability the given action was taken.

  • **kwargs – Optional information returned during prediction.

predict(context: None | str | Number | Sequence | Mapping, actions: None | Sequence[Action]) → Tuple[Action, Prob]

Predict which action to take in the context.

Parameters:

  • context – The current context. It will either be None (multi-armed bandit), a value (a single feature), a sequence of values (dense features), or a dictionary (sparse features).

  • actions – The current set of actions to choose from in the given context. Each action will either be a value (a single feature), a sequence of values (dense features), or a dictionary (sparse features).

Returns:

A Prediction. Several prediction formats are supported. See the type-hint for these.

score(context: None | str | Number | Sequence | Mapping, actions: None | Sequence[Action], action: str | Number | Sequence | Mapping) → float

Propensity score an action.

Parameters:

  • context – The current context.

  • actions – The current set of actions that can be chosen.

  • action – The action to propensity score.

Returns:

The propensity score of the given action. That is, P(action|context,actions).

Attributes

params