diff --git a/d3rlpy/algos/qlearning/base.py b/d3rlpy/algos/qlearning/base.py
index e6de38b0..ba92f3d3 100644
--- a/d3rlpy/algos/qlearning/base.py
+++ b/d3rlpy/algos/qlearning/base.py
@@ -18,7 +18,11 @@
 from typing_extensions import Self
 
 from ...base import ImplBase, LearnableBase, LearnableConfig, save_config
-from ...constants import IMPL_NOT_INITIALIZED_ERROR, ActionSpace
+from ...constants import (
+    IMPL_NOT_INITIALIZED_ERROR,
+    ActionSpace,
+    LoggingStrategy,
+)
 from ...dataset import (
     ReplayBuffer,
     TransitionMiniBatch,
@@ -360,6 +364,8 @@ def fit(
         n_steps_per_epoch: int = 10000,
         experiment_name: Optional[str] = None,
         with_timestamp: bool = True,
+        logging_steps: int = 500,
+        logging_strategy: LoggingStrategy = LoggingStrategy.EPOCH,
         logger_adapter: LoggerAdapterFactory = FileAdapterFactory(),
         show_progress: bool = True,
         save_interval: int = 1,
@@ -383,6 +389,8 @@ def fit(
                 the directory name will be `{class name}_{timestamp}`.
             with_timestamp: Flag to add timestamp string to the last of
                 directory name.
+            logging_steps: number of steps to log metrics.
+            logging_strategy: what logging strategy to use.
             logger_adapter: LoggerAdapterFactory object.
             show_progress: Flag to show progress bar for iterations.
             save_interval: Interval to save parameters.
@@ -404,6 +412,8 @@ def fit(
                 n_steps_per_epoch=n_steps_per_epoch,
                 experiment_name=experiment_name,
                 with_timestamp=with_timestamp,
+                logging_steps=logging_steps,
+                logging_strategy=logging_strategy,
                 logger_adapter=logger_adapter,
                 show_progress=show_progress,
                 save_interval=save_interval,
@@ -420,6 +430,8 @@ def fitter(
         dataset: ReplayBuffer,
         n_steps: int,
         n_steps_per_epoch: int = 10000,
+        logging_steps: int = 500,
+        logging_strategy: LoggingStrategy = LoggingStrategy.EPOCH,
         experiment_name: Optional[str] = None,
         with_timestamp: bool = True,
         logger_adapter: LoggerAdapterFactory = FileAdapterFactory(),
@@ -448,6 +460,8 @@ def fitter(
                 the directory name will be `{class name}_{timestamp}`.
             with_timestamp: Flag to add timestamp string to the last of
                 directory name.
+            logging_steps: number of steps to log metrics.
+            logging_strategy: what logging strategy to use.
             logger_adapter: LoggerAdapterFactory object.
             show_progress: Flag to show progress bar for iterations.
             save_interval: Interval to save parameters.
@@ -540,6 +554,12 @@ def fitter(
 
                 total_step += 1
 
+                if (
+                    logging_strategy == LoggingStrategy.STEPS
+                    and total_step % logging_steps == 0
+                ):
+                    metrics = logger.commit(epoch, total_step)
+
                 # call callback if given
                 if callback:
                     callback(self, epoch, total_step)
@@ -554,7 +574,8 @@ def fitter(
                     logger.add_metric(name, test_score)
 
             # save metrics
-            metrics = logger.commit(epoch, total_step)
+            if logging_strategy == LoggingStrategy.EPOCH:
+                metrics = logger.commit(epoch, total_step)
 
             # save model parameters
             if epoch % save_interval == 0:
diff --git a/d3rlpy/constants.py b/d3rlpy/constants.py
index 9b233760..6e63c2a6 100644
--- a/d3rlpy/constants.py
+++ b/d3rlpy/constants.py
@@ -47,3 +47,8 @@ class ActionSpace(Enum):
 class PositionEncodingType(Enum):
     SIMPLE = "simple"
     GLOBAL = "global"
+
+
+class LoggingStrategy(Enum):
+    STEPS = "steps"
+    EPOCH = "epoch"
diff --git a/d3rlpy/logging/wandb_adapter.py b/d3rlpy/logging/wandb_adapter.py
new file mode 100644
index 00000000..373986eb
--- /dev/null
+++ b/d3rlpy/logging/wandb_adapter.py
@@ -0,0 +1,79 @@
+from typing import Any, Dict, Optional
+from .logger import LoggerAdapter, LoggerAdapterFactory, SaveProtocol
+
+
+__all__ = ["LoggerWanDBAdapter", "WanDBAdapterFactory"]
+
+
+class LoggerWanDBAdapter(LoggerAdapter):
+    r"""WandB Logger Adapter class.
+
+    This class logs data to Weights & Biases (WandB) for experiment tracking.
+
+    Args:
+        experiment_name (str): Name of the experiment.
+    """
+
+    def __init__(self, project: Optional[str] = None, experiment_name: Optional[str] = None):
+        try:
+            import wandb
+        except ImportError as e:
+            raise ImportError("Please install wandb") from e
+        self.run = wandb.init(project=project, name=experiment_name)
+
+    def write_params(self, params: Dict[str, Any]) -> None:
+        """Writes hyperparameters to WandB config."""
+        self.run.config.update(params)
+
+    def before_write_metric(self, epoch: int, step: int) -> None:
+        """Callback executed before writing metric."""
+        pass
+
+    def write_metric(self, epoch: int, step: int, name: str, value: float) -> None:
+        """Writes metric to WandB."""
+        self.run.log({name: value, 'epoch': epoch}, step=step)
+
+    def after_write_metric(self, epoch: int, step: int) -> None:
+        """Callback executed after writing metric."""
+        pass
+
+    def save_model(self, epoch: int, algo: SaveProtocol) -> None:
+        """Saves models to Weights & Biases. Not implemented for WandB."""
+        # Implement saving model to wandb if needed
+        pass
+
+    def close(self) -> None:
+        """Closes the logger and finishes the WandB run."""
+        self.run.finish()
+
+
+class WanDBAdapterFactory(LoggerAdapterFactory):
+    r"""WandB Logger Adapter Factory class.
+
+    This class creates instances of the WandB Logger Adapter for experiment tracking.
+
+    """
+
+    _project: str
+
+    def __init__(self, project: Optional[str] = None) -> None:
+        """Initialize the WandB Logger Adapter Factory.
+
+        Args:
+            project (Optional[str], optional): The name of the WandB project. Defaults to None.
+
+        """
+        super().__init__()
+        self._project = project
+
+    def create(self, experiment_name: str) -> LoggerAdapter:
+        """Creates a WandB Logger Adapter instance.
+
+        Args:
+            experiment_name (str): Name of the experiment.
+
+        Returns:
+            LoggerAdapter: Instance of the WandB Logger Adapter.
+
+        """
+        return LoggerWanDBAdapter(project=self._project, experiment_name=experiment_name)