(self, sample_lists)
Run iteration of PILQR.
        Args:
        sample_lists: List of SampleList objects for each condition.
Run iteration of PILQR.
        Args:
        sample_lists: List of SampleList objects for each condition.
Run iteration of PILQR . Args : sample_lists : List of SampleList objects for each condition .
def iteration(self, sample_lists):
        """
        Run iteration of PILQR.
        Args:
        sample_lists: List of SampleList objects for each condition.
        """
        for m in range(self.M):
            self.cur[m].sample_list = sample_lists[m]
            self._eval_cost(m)
        
        # Update dynamics model using all samples.
        self._update_dynamics()
        
        # On the first iteration, need to catch policy up to init_traj_distr.
        if self.iteration_count == 0:
            self.new_traj_distr = [
                self.cur[cond].traj_distr for cond in range(self.M)
            ]
            AlgorithmMDGPS._update_policy(self)
        
        # Update policy linearizations.
        for m in range(self.M):
            AlgorithmMDGPS._update_policy_fit(self, m)

        # C-step
        if self.iteration_count > 0:
            for m in range(self.M):
                AlgorithmTrajOptPILQR._stepadjust(self, m)
		
        self._update_trajectories()

        # S-step
        self._update_policy()

        # Prepare for next iteration
        self._advance_iteration_variables()
def iteration ( self , sample_lists ) : for m in range ( self . M ) : self . cur [ m ] . sample_list = sample_lists [ m ] self . _eval_cost ( m ) # Update dynamics model using all samples. self . _update_dynamics ( ) # On the first iteration, need to catch policy up to init_traj_distr. if self . iteration_count == 0 : self . new_traj_distr = [ self . cur [ cond ] . traj_distr for cond in range ( self . M ) ] AlgorithmMDGPS . _update_policy ( self ) # Update policy linearizations. for m in range ( self . M ) : AlgorithmMDGPS . _update_policy_fit ( self , m ) # C-step if self . iteration_count > 0 : for m in range ( self . M ) : AlgorithmTrajOptPILQR . _stepadjust ( self , m ) self . _update_trajectories ( ) # S-step self . _update_policy ( ) # Prepare for next iteration self . _advance_iteration_variables ( )