tensorforce/agents/agent.py
def observe(self, terminal, reward, index=0):
        """
        Observe experience from the environment to learn from. Optionally pre-processes rewards
        Child classes should call super to get the processed reward
        EX: terminal, reward = super()...

        Args:
            terminal (bool): boolean indicating if the episode terminated after the observation.
            reward (float): scalar reward that resulted from executing the action.
        """
        self.current_terminal = terminal
        self.current_reward = reward

        if self.batched_observe:
            # Batched observe for better performance with Python.
            self.observe_terminal[index].append(self.current_terminal)
            self.observe_reward[index].append(self.current_reward)

            if self.current_terminal or len(self.observe_terminal[index]) >= self.batching_capacity:
                self.episode = self.model.observe(
                    terminal=self.observe_terminal[index],
                    reward=self.observe_reward[index],
                    index=index
                )
                self.observe_terminal[index] = list()
                self.observe_reward[index] = list()

        else:
            self.episode = self.model.observe(
                terminal=self.current_terminal,
                reward=self.current_reward
            )
def observe(self, terminal, reward, index=0):
        """
        Observe experience from the environment to learn from. Optionally pre-processes rewards
        Child classes should call super to get the processed reward
        EX: terminal, reward = super()...

        Args:
            terminal (bool): boolean indicating if the episode terminated after the observation.
            reward (float): scalar reward that resulted from executing the action.
        """
        self.current_terminal = terminal
        self.current_reward = reward

        if self.batched_observe:
            # Batched observe for better performance with Python.
            self.observe_terminal[index].append(self.current_terminal)
            self.observe_reward[index].append(self.current_reward)

            if self.current_terminal or len(self.observe_terminal[index]) >= self.batching_capacity:
                self.episode = self.model.observe(
                    terminal=self.observe_terminal[index],
                    reward=self.observe_reward[index],
                    index=index
                )
                self.observe_terminal[index] = list()
                self.observe_reward[index] = list()

        else:
            self.episode = self.model.observe(
                terminal=self.current_terminal,
                reward=self.current_reward
            )
def observe ( self , terminal , reward , index = 0 ) : self . current_terminal = terminal self . current_reward = reward if self . batched_observe : # Batched observe for better performance with Python. self . observe_terminal [ index ] . append ( self . current_terminal ) self . observe_reward [ index ] . append ( self . current_reward ) if self . current_terminal or len ( self . observe_terminal [ index ] ) >= self . batching_capacity : self . episode = self . model . observe ( terminal = self . observe_terminal [ index ] , reward = self . observe_reward [ index ] , index = index ) self . observe_terminal [ index ] = list ( ) self . observe_reward [ index ] = list ( ) else : self . episode = self . model . observe ( terminal = self . current_terminal , reward = self . current_reward )
Observe experience from the environment to learn from. Optionally pre-processes rewards
        Child classes should call super to get the processed reward
        EX: terminal, reward = super()...

        Args:
            terminal (bool): boolean indicating if the episode terminated after the observation.
            reward (float): scalar reward that resulted from executing the action.
Observe experience from the environment to learn from . Optionally pre - processes rewards Child classes should call super to get the processed reward EX : terminal reward = super () ...