def backward(self, reward, terminal):
raise NotImplementedError()<EOL>
Updates the agent after having executed the action returned by `forward`.
        If the policy is implemented by a neural network, this corresponds to a weight update using back-prop.

        # Argument
            reward (float): The observed reward after executing the action returned by `forward`.
            terminal (boolean): `True` if the new state of the environment is terminal.

        # Returns
            List of metrics values