def setup_placeholders(self):
        """
        Creates the TensorFlow placeholders, variables, ops and functions for this model.
        NOTE: Does not add the internal state placeholders and initialization values to the model yet as that requires
        the model's Network (if any) to be generated first.
        """

        # States
        for name in sorted(self.states_spec):
            self.states_input[name] = tf.placeholder(
                dtype=util.tf_dtype(self.states_spec[name]['type']),
                shape=(None,) + tuple(self.states_spec[name]['shape']),
                name=('state-' + name)
            )

        # States preprocessing
        if self.states_preprocessing_spec is None:
            for name in sorted(self.states_spec):
                self.states_spec[name]['unprocessed_shape'] = self.states_spec[name]['shape']
        elif not isinstance(self.states_preprocessing_spec, list) and \
                all(name in self.states_spec for name in self.states_preprocessing_spec):
            for name in sorted(self.states_spec):
                if name in self.states_preprocessing_spec:
                    preprocessing = PreprocessorStack.from_spec(
                        spec=self.states_preprocessing_spec[name],
                        kwargs=dict(shape=self.states_spec[name]['shape'])
                    )
                    self.states_spec[name]['unprocessed_shape'] = self.states_spec[name]['shape']
                    self.states_spec[name]['shape'] = preprocessing.processed_shape(shape=self.states_spec[name]['unprocessed_shape'])
                    self.states_preprocessing[name] = preprocessing
                else:
                    self.states_spec[name]['unprocessed_shape'] = self.states_spec[name]['shape']
        # Single preprocessor for all components of our state space
        elif "type" in self.states_preprocessing_spec:
            preprocessing = PreprocessorStack.from_spec(spec=self.states_preprocessing_spec,
                                                        kwargs=dict(shape=self.states_spec[name]['shape']))
            for name in sorted(self.states_spec):
                self.states_spec[name]['unprocessed_shape'] = self.states_spec[name]['shape']
                self.states_spec[name]['shape'] = preprocessing.processed_shape(shape=self.states_spec[name]['unprocessed_shape'])
                self.states_preprocessing[name] = preprocessing
        else:
            for name in sorted(self.states_spec):
                preprocessing = PreprocessorStack.from_spec(
                    spec=self.states_preprocessing_spec,
                    kwargs=dict(shape=self.states_spec[name]['shape'])
                )
                self.states_spec[name]['unprocessed_shape'] = self.states_spec[name]['shape']
                self.states_spec[name]['shape'] = preprocessing.processed_shape(shape=self.states_spec[name]['unprocessed_shape'])
                self.states_preprocessing[name] = preprocessing

        # Actions
        for name in sorted(self.actions_spec):
            self.actions_input[name] = tf.placeholder(
                dtype=util.tf_dtype(self.actions_spec[name]['type']),
                shape=(None,) + tuple(self.actions_spec[name]['shape']),
                name=('action-' + name)
            )

        # Actions exploration
        if self.actions_exploration_spec is None:
            pass
        elif all(name in self.actions_spec for name in self.actions_exploration_spec):
            for name in sorted(self.actions_spec):
                if name in self.actions_exploration:
                    self.actions_exploration[name] = Exploration.from_spec(spec=self.actions_exploration_spec[name])
        else:
            for name in sorted(self.actions_spec):
                self.actions_exploration[name] = Exploration.from_spec(spec=self.actions_exploration_spec)

        # Terminal
        self.terminal_input = tf.placeholder(dtype=util.tf_dtype('bool'), shape=(None,), name='terminal')

        # Reward
        self.reward_input = tf.placeholder(dtype=util.tf_dtype('float'), shape=(None,), name='reward')

        # Reward preprocessing
        if self.reward_preprocessing_spec is not None:
            self.reward_preprocessing = PreprocessorStack.from_spec(
                spec=self.reward_preprocessing_spec,
                # TODO this can eventually have more complex shapes?
                kwargs=dict(shape=())
            )
            if self.reward_preprocessing.processed_shape(shape=()) != ():
                raise TensorForceError("Invalid reward preprocessing!")

        # Deterministic/independent action flag (should probably be the same)
        self.deterministic_input = tf.placeholder(dtype=util.tf_dtype('bool'), shape=(), name='deterministic')
        self.independent_input = tf.placeholder(dtype=util.tf_dtype('bool'), shape=(), name='independent')
def setup_placeholders(self):
        """
        Creates the TensorFlow placeholders, variables, ops and functions for this model.
        NOTE: Does not add the internal state placeholders and initialization values to the model yet as that requires
        the model's Network (if any) to be generated first.
        """

        # States
        for name in sorted(self.states_spec):
            self.states_input[name] = tf.placeholder(
                dtype=util.tf_dtype(self.states_spec[name]['type']),
                shape=(None,) + tuple(self.states_spec[name]['shape']),
                name=('state-' + name)
            )

        # States preprocessing
        if self.states_preprocessing_spec is None:
            for name in sorted(self.states_spec):
                self.states_spec[name]['unprocessed_shape'] = self.states_spec[name]['shape']
        elif not isinstance(self.states_preprocessing_spec, list) and \
                all(name in self.states_spec for name in self.states_preprocessing_spec):
            for name in sorted(self.states_spec):
                if name in self.states_preprocessing_spec:
                    preprocessing = PreprocessorStack.from_spec(
                        spec=self.states_preprocessing_spec[name],
                        kwargs=dict(shape=self.states_spec[name]['shape'])
                    )
                    self.states_spec[name]['unprocessed_shape'] = self.states_spec[name]['shape']
                    self.states_spec[name]['shape'] = preprocessing.processed_shape(shape=self.states_spec[name]['unprocessed_shape'])
                    self.states_preprocessing[name] = preprocessing
                else:
                    self.states_spec[name]['unprocessed_shape'] = self.states_spec[name]['shape']
        # Single preprocessor for all components of our state space
        elif "type" in self.states_preprocessing_spec:
            preprocessing = PreprocessorStack.from_spec(spec=self.states_preprocessing_spec,
                                                        kwargs=dict(shape=self.states_spec[name]['shape']))
            for name in sorted(self.states_spec):
                self.states_spec[name]['unprocessed_shape'] = self.states_spec[name]['shape']
                self.states_spec[name]['shape'] = preprocessing.processed_shape(shape=self.states_spec[name]['unprocessed_shape'])
                self.states_preprocessing[name] = preprocessing
        else:
            for name in sorted(self.states_spec):
                preprocessing = PreprocessorStack.from_spec(
                    spec=self.states_preprocessing_spec,
                    kwargs=dict(shape=self.states_spec[name]['shape'])
                )
                self.states_spec[name]['unprocessed_shape'] = self.states_spec[name]['shape']
                self.states_spec[name]['shape'] = preprocessing.processed_shape(shape=self.states_spec[name]['unprocessed_shape'])
                self.states_preprocessing[name] = preprocessing

        # Actions
        for name in sorted(self.actions_spec):
            self.actions_input[name] = tf.placeholder(
                dtype=util.tf_dtype(self.actions_spec[name]['type']),
                shape=(None,) + tuple(self.actions_spec[name]['shape']),
                name=('action-' + name)
            )

        # Actions exploration
        if self.actions_exploration_spec is None:
            pass
        elif all(name in self.actions_spec for name in self.actions_exploration_spec):
            for name in sorted(self.actions_spec):
                if name in self.actions_exploration:
                    self.actions_exploration[name] = Exploration.from_spec(spec=self.actions_exploration_spec[name])
        else:
            for name in sorted(self.actions_spec):
                self.actions_exploration[name] = Exploration.from_spec(spec=self.actions_exploration_spec)

        # Terminal
        self.terminal_input = tf.placeholder(dtype=util.tf_dtype('bool'), shape=(None,), name='terminal')

        # Reward
        self.reward_input = tf.placeholder(dtype=util.tf_dtype('float'), shape=(None,), name='reward')

        # Reward preprocessing
        if self.reward_preprocessing_spec is not None:
            self.reward_preprocessing = PreprocessorStack.from_spec(
                spec=self.reward_preprocessing_spec,
                # TODO this can eventually have more complex shapes?
                kwargs=dict(shape=())
            )
            if self.reward_preprocessing.processed_shape(shape=()) != ():
                raise TensorForceError("Invalid reward preprocessing!")

        # Deterministic/independent action flag (should probably be the same)
        self.deterministic_input = tf.placeholder(dtype=util.tf_dtype('bool'), shape=(), name='deterministic')
        self.independent_input = tf.placeholder(dtype=util.tf_dtype('bool'), shape=(), name='independent')
def setup_placeholders ( self ) : # States for name in sorted ( self . states_spec ) : self . states_input [ name ] = tf . placeholder ( dtype = util . tf_dtype ( self . states_spec [ name ] [ 'type' ] ) , shape = ( None , ) + tuple ( self . states_spec [ name ] [ 'shape' ] ) , name = ( 'state-' + name ) ) # States preprocessing if self . states_preprocessing_spec is None : for name in sorted ( self . states_spec ) : self . states_spec [ name ] [ 'unprocessed_shape' ] = self . states_spec [ name ] [ 'shape' ] elif not isinstance ( self . states_preprocessing_spec , list ) and all ( name in self . states_spec for name in self . states_preprocessing_spec ) : for name in sorted ( self . states_spec ) : if name in self . states_preprocessing_spec : preprocessing = PreprocessorStack . from_spec ( spec = self . states_preprocessing_spec [ name ] , kwargs = dict ( shape = self . states_spec [ name ] [ 'shape' ] ) ) self . states_spec [ name ] [ 'unprocessed_shape' ] = self . states_spec [ name ] [ 'shape' ] self . states_spec [ name ] [ 'shape' ] = preprocessing . processed_shape ( shape = self . states_spec [ name ] [ 'unprocessed_shape' ] ) self . states_preprocessing [ name ] = preprocessing else : self . states_spec [ name ] [ 'unprocessed_shape' ] = self . states_spec [ name ] [ 'shape' ] # Single preprocessor for all components of our state space elif "type" in self . states_preprocessing_spec : preprocessing = PreprocessorStack . from_spec ( spec = self . states_preprocessing_spec , kwargs = dict ( shape = self . states_spec [ name ] [ 'shape' ] ) ) for name in sorted ( self . states_spec ) : self . states_spec [ name ] [ 'unprocessed_shape' ] = self . states_spec [ name ] [ 'shape' ] self . states_spec [ name ] [ 'shape' ] = preprocessing . processed_shape ( shape = self . states_spec [ name ] [ 'unprocessed_shape' ] ) self . states_preprocessing [ name ] = preprocessing else : for name in sorted ( self . states_spec ) : preprocessing = PreprocessorStack . from_spec ( spec = self . states_preprocessing_spec , kwargs = dict ( shape = self . states_spec [ name ] [ 'shape' ] ) ) self . states_spec [ name ] [ 'unprocessed_shape' ] = self . states_spec [ name ] [ 'shape' ] self . states_spec [ name ] [ 'shape' ] = preprocessing . processed_shape ( shape = self . states_spec [ name ] [ 'unprocessed_shape' ] ) self . states_preprocessing [ name ] = preprocessing # Actions for name in sorted ( self . actions_spec ) : self . actions_input [ name ] = tf . placeholder ( dtype = util . tf_dtype ( self . actions_spec [ name ] [ 'type' ] ) , shape = ( None , ) + tuple ( self . actions_spec [ name ] [ 'shape' ] ) , name = ( 'action-' + name ) ) # Actions exploration if self . actions_exploration_spec is None : pass elif all ( name in self . actions_spec for name in self . actions_exploration_spec ) : for name in sorted ( self . actions_spec ) : if name in self . actions_exploration : self . actions_exploration [ name ] = Exploration . from_spec ( spec = self . actions_exploration_spec [ name ] ) else : for name in sorted ( self . actions_spec ) : self . actions_exploration [ name ] = Exploration . from_spec ( spec = self . actions_exploration_spec ) # Terminal self . terminal_input = tf . placeholder ( dtype = util . tf_dtype ( 'bool' ) , shape = ( None , ) , name = 'terminal' ) # Reward self . reward_input = tf . placeholder ( dtype = util . tf_dtype ( 'float' ) , shape = ( None , ) , name = 'reward' ) # Reward preprocessing if self . reward_preprocessing_spec is not None : self . reward_preprocessing = PreprocessorStack . from_spec ( spec = self . reward_preprocessing_spec , # TODO this can eventually have more complex shapes? kwargs = dict ( shape = ( ) ) ) if self . reward_preprocessing . processed_shape ( shape = ( ) ) != ( ) : raise TensorForceError ( "Invalid reward preprocessing!" ) # Deterministic/independent action flag (should probably be the same) self . deterministic_input = tf . placeholder ( dtype = util . tf_dtype ( 'bool' ) , shape = ( ) , name = 'deterministic' ) self . independent_input = tf . placeholder ( dtype = util . tf_dtype ( 'bool' ) , shape = ( ) , name = 'independent' )
Creates the TensorFlow placeholders, variables, ops and functions for this model.
        NOTE: Does not add the internal state placeholders and initialization values to the model yet as that requires
        the model's Network (if any) to be generated first.
Creates the TensorFlow placeholders variables ops and functions for this model . NOTE : Does not add the internal state placeholders and initialization values to the model yet as that requires the model s Network ( if any ) to be generated first .