src/model/ddpg/ddpg.py
def predict(self, observation):
        """ predict the next action using actor model, only used in deploy.
            Can be used in multiple environments.

        Args:
            observation: (batch_size, num_stocks + 1, window_length)

        Returns: action array with shape (batch_size, num_stocks + 1)

        """
        if self.obs_normalizer:
            observation = self.obs_normalizer(observation)
        action = self.actor.predict(observation)
        if self.action_processor:
            action = self.action_processor(action)
        return action
 predict the next action using actor model, only used in deploy.
            Can be used in multiple environments.

        Args:
            observation: (batch_size, num_stocks + 1, window_length)

        Returns: action array with shape (batch_size, num_stocks + 1)

        
predict the next action using actor model, only used in deploy.
Can be used in multiple environments.
predict the next action using actor model only used in deploy . Can be used in multiple environments .
def predict(self, observation):
        if self.obs_normalizer:
            observation = self.obs_normalizer(observation)
        action = self.actor.predict(observation)
        if self.action_processor:
            action = self.action_processor(action)
        return action
def predict ( self , observation ) : if self . obs_normalizer : observation = self . obs_normalizer ( observation ) action = self . actor . predict ( observation ) if self . action_processor : action = self . action_processor ( action ) return action
predict the next action using actor model, only used in deploy.
predict the next action using actor model only used in deploy .
""" predict the next action using actor model, only used in deploy.
            Can be used in multiple environments.

        Args:
            observation: (batch_size, num_stocks + 1, window_length)

        Returns: action array with shape (batch_size, num_stocks + 1)

        """
self
observation
(batch_size, num_stocks + 1, window_length)
( batch_size num_stocks + 1 window_length )