dqn.py
def update(self, x_stack: np.ndarray, y_stack: np.ndarray) -> list:
        """Performs updates on given X and y and returns a result

        Args:
            x_stack (np.ndarray): State array, shape (n, input_dim)
            y_stack (np.ndarray): Target Q array, shape (n, output_dim)

        Returns:
            list: First element is loss, second element is a result from train step
        """
        feed = {
            self._X: x_stack,
            self._Y: y_stack
        }
        return self.session.run([self._loss, self._train], feed)
Performs updates on given X and y and returns a result

        Args:
            x_stack (np.ndarray): State array, shape (n, input_dim)
            y_stack (np.ndarray): Target Q array, shape (n, output_dim)

        Returns:
            list: First element is loss, second element is a result from train step
        
Performs updates on given X and y and returns a result
Performs updates on given X and y and returns a result
def update(self, x_stack: np.ndarray, y_stack: np.ndarray) -> list:
        feed = {
            self._X: x_stack,
            self._Y: y_stack
        }
        return self.session.run([self._loss, self._train], feed)
def update ( self , x_stack : np . ndarray , y_stack : np . ndarray ) -> list : feed = { self . _X : x_stack , self . _Y : y_stack } return self . session . run ( [ self . _loss , self . _train ] , feed )
Performs updates on given X and y and returns a result
Performs updates on given X and y and returns a result
"""Performs updates on given X and y and returns a result

        Args:
            x_stack (np.ndarray): State array, shape (n, input_dim)
            y_stack (np.ndarray): Target Q array, shape (n, output_dim)

        Returns:
            list: First element is loss, second element is a result from train step
        """
First element is loss, second element is a result from train step
First element is loss second element is a result from train step
list
self
x_stack
np.ndarray
State array, shape (n, input_dim)
State array shape ( n input_dim )
y_stack
np.ndarray
Target Q array, shape (n, output_dim)
Target Q array shape ( n output_dim )