tensorforce/core/memories/deprecated/deprecated_prioritized_replay.py
def put(self, item, priority=None):
        """
        Stores a transition in replay memory.

        If the memory is full, the oldest entry is replaced.
        """
        if not self._isfull():
            self._memory.append(None)
        position = self._next_position_then_increment()
        old_priority = 0 if self._memory[position] is None \
            else (self._memory[position].priority or 0)
        row = _SumRow(item, priority)
        self._memory[position] = row
        self._update_internal_nodes(
            position, (row.priority or 0) - old_priority)
def put(self, item, priority=None):
        """
        Stores a transition in replay memory.

        If the memory is full, the oldest entry is replaced.
        """
        if not self._isfull():
            self._memory.append(None)
        position = self._next_position_then_increment()
        old_priority = 0 if self._memory[position] is None \
            else (self._memory[position].priority or 0)
        row = _SumRow(item, priority)
        self._memory[position] = row
        self._update_internal_nodes(
            position, (row.priority or 0) - old_priority)
def put ( self , item , priority = None ) : if not self . _isfull ( ) : self . _memory . append ( None ) position = self . _next_position_then_increment ( ) old_priority = 0 if self . _memory [ position ] is None else ( self . _memory [ position ] . priority or 0 ) row = _SumRow ( item , priority ) self . _memory [ position ] = row self . _update_internal_nodes ( position , ( row . priority or 0 ) - old_priority )
Stores a transition in replay memory.

        If the memory is full, the oldest entry is replaced.
Stores a transition in replay memory .