tensorforce/agents/dqfd_agent.py
def import_demonstrations(self, demonstrations):
        """
        Imports demonstrations, i.e. expert observations. Note that for large numbers of observations,
        set_demonstrations is more appropriate, which directly sets memory contents to an array an expects
        a different layout.

        Args:
            demonstrations: List of observation dicts
        """
        if isinstance(demonstrations, dict):
            if self.unique_state:
                demonstrations['states'] = dict(state=demonstrations['states'])
            if self.unique_action:
                demonstrations['actions'] = dict(action=demonstrations['actions'])

            self.model.import_demo_experience(**demonstrations)

        else:
            if self.unique_state:
                states = dict(state=list())
            else:
                states = {name: list() for name in demonstrations[0]['states']}
            internals = {name: list() for name in demonstrations[0]['internals']}
            if self.unique_action:
                actions = dict(action=list())
            else:
                actions = {name: list() for name in demonstrations[0]['actions']}
            terminal = list()
            reward = list()

            for demonstration in demonstrations:
                if self.unique_state:
                    states['state'].append(demonstration['states'])
                else:
                    for name, state in states.items():
                        state.append(demonstration['states'][name])
                for name, internal in internals.items():
                    internal.append(demonstration['internals'][name])
                if self.unique_action:
                    actions['action'].append(demonstration['actions'])
                else:
                    for name, action in actions.items():
                        action.append(demonstration['actions'][name])
                terminal.append(demonstration['terminal'])
                reward.append(demonstration['reward'])

            self.model.import_demo_experience(
                states=states,
                internals=internals,
                actions=actions,
                terminal=terminal,
                reward=reward
            )
def import_demonstrations(self, demonstrations):
        """
        Imports demonstrations, i.e. expert observations. Note that for large numbers of observations,
        set_demonstrations is more appropriate, which directly sets memory contents to an array an expects
        a different layout.

        Args:
            demonstrations: List of observation dicts
        """
        if isinstance(demonstrations, dict):
            if self.unique_state:
                demonstrations['states'] = dict(state=demonstrations['states'])
            if self.unique_action:
                demonstrations['actions'] = dict(action=demonstrations['actions'])

            self.model.import_demo_experience(**demonstrations)

        else:
            if self.unique_state:
                states = dict(state=list())
            else:
                states = {name: list() for name in demonstrations[0]['states']}
            internals = {name: list() for name in demonstrations[0]['internals']}
            if self.unique_action:
                actions = dict(action=list())
            else:
                actions = {name: list() for name in demonstrations[0]['actions']}
            terminal = list()
            reward = list()

            for demonstration in demonstrations:
                if self.unique_state:
                    states['state'].append(demonstration['states'])
                else:
                    for name, state in states.items():
                        state.append(demonstration['states'][name])
                for name, internal in internals.items():
                    internal.append(demonstration['internals'][name])
                if self.unique_action:
                    actions['action'].append(demonstration['actions'])
                else:
                    for name, action in actions.items():
                        action.append(demonstration['actions'][name])
                terminal.append(demonstration['terminal'])
                reward.append(demonstration['reward'])

            self.model.import_demo_experience(
                states=states,
                internals=internals,
                actions=actions,
                terminal=terminal,
                reward=reward
            )
def import_demonstrations ( self , demonstrations ) : if isinstance ( demonstrations , dict ) : if self . unique_state : demonstrations [ 'states' ] = dict ( state = demonstrations [ 'states' ] ) if self . unique_action : demonstrations [ 'actions' ] = dict ( action = demonstrations [ 'actions' ] ) self . model . import_demo_experience ( * * demonstrations ) else : if self . unique_state : states = dict ( state = list ( ) ) else : states = { name : list ( ) for name in demonstrations [ 0 ] [ 'states' ] } internals = { name : list ( ) for name in demonstrations [ 0 ] [ 'internals' ] } if self . unique_action : actions = dict ( action = list ( ) ) else : actions = { name : list ( ) for name in demonstrations [ 0 ] [ 'actions' ] } terminal = list ( ) reward = list ( ) for demonstration in demonstrations : if self . unique_state : states [ 'state' ] . append ( demonstration [ 'states' ] ) else : for name , state in states . items ( ) : state . append ( demonstration [ 'states' ] [ name ] ) for name , internal in internals . items ( ) : internal . append ( demonstration [ 'internals' ] [ name ] ) if self . unique_action : actions [ 'action' ] . append ( demonstration [ 'actions' ] ) else : for name , action in actions . items ( ) : action . append ( demonstration [ 'actions' ] [ name ] ) terminal . append ( demonstration [ 'terminal' ] ) reward . append ( demonstration [ 'reward' ] ) self . model . import_demo_experience ( states = states , internals = internals , actions = actions , terminal = terminal , reward = reward )
Imports demonstrations, i.e. expert observations. Note that for large numbers of observations,
        set_demonstrations is more appropriate, which directly sets memory contents to an array an expects
        a different layout.

        Args:
            demonstrations: List of observation dicts
Imports demonstrations i . e . expert observations . Note that for large numbers of observations set_demonstrations is more appropriate which directly sets memory contents to an array an expects a different layout .