Improve LinearBandit documentation

Summary: Improve LinearBandit documentation

Reviewed By: jb3618columbia

Differential Revision: D62759074

fbshipit-source-id: ee361128334c2ce327d373c8a6c57b74df18998b

pearl/policy_learners/contextual_bandits/linear_bandit.py

@@ -37,7 +37,29 @@
 
 class LinearBandit(ContextualBanditBase):
     """
-    Policy Learner for Contextual Bandit with Linear Policy
+    Policy Learner for Contextual Bandit with Linear Policy.
+
+    This class implements a policy learner for a contextual bandit problem where the policy is
+    linear. It supports learning through linear regression and can apply discounting to observations
+    based on the number of weighted data points processed. The learner also supports exploration
+    modules for acting based on learned policies.
+
+    Attributes:
+        model (LinearRegression): Linear regression model used for learning.
+        apply_discounting_interval (float): Interval for applying discounting to the data points.
+        last_sum_weight_when_discounted (float): The counter for the last data point when discounting was applied.
+
+    Args:
+        feature_dim (int): Dimension of the feature space.
+        exploration_module (Optional[ExplorationModule]): Module for exploring actions.
+        l2_reg_lambda (float): L2 regularization parameter for the linear regression model.
+        gamma (float): Discount factor for discounting observations.
+        apply_discounting_interval (float): number of (weighted observations) for applying discounting to the data points.
+                                            Set to 0.0 to disable.
+        force_pinv (bool): If True, use pseudo-inverse for matrix inversion in the linear model.
+        training_rounds (int): Number of training rounds.
+        batch_size (int): Size of the batches used during training.
+        action_representation_module (Optional[ActionRepresentationModule]): Module for representing actions.
     """
 
     def __init__(
@@ -46,9 +68,8 @@ def __init__(
         exploration_module: Optional[ExplorationModule] = None,
         l2_reg_lambda: float = 1.0,
         gamma: float = 1.0,
-        apply_discounting_interval: float = 0.0,  # discounting will be applied after this many
-        # observations (weighted) are processed. set to 0 to disable
-        force_pinv: bool = False,  # If True, use pseudo inverse instead of regular inverse for `A`
+        apply_discounting_interval: float = 0.0,
+        force_pinv: bool = False,
         training_rounds: int = 100,
         batch_size: int = 128,
         action_representation_module: Optional[ActionRepresentationModule] = None,