major redesign ob observations and entittes

algorithms/static/TSP_item_agent.py

@@ -0,0 +1,59 @@
+import numpy as np
+
+from algorithms.static.TSP_base_agent import TSPBaseAgent
+
+from modules.items import constants as i
+
+future_planning = 7
+inventory_size  = 3
+
+MODE_GET        = 'Mode_Get'
+MODE_BRING      = 'Mode_Bring'
+
+
+class TSPItemAgent(TSPBaseAgent):
+
+    def __init__(self, *args, mode=MODE_GET, **kwargs):
+        super(TSPItemAgent, self).__init__(*args, **kwargs)
+        self.mode = mode
+
+    def predict(self, *_, **__):
+        if self._env.state[i.ITEM].by_pos(self.state.pos) is not None:
+            # Translate the action_object to an integer to have the same output as any other model
+            action = i.ITEM_ACTION
+        elif self._env.state[i.DROP_OFF].by_pos(self.state.pos) is not None:
+            # Translate the action_object to an integer to have the same output as any other model
+            action = i.ITEM_ACTION
+        elif door := self._door_is_close():
+            action = self._use_door_or_move(door, i.DROP_OFF if self.mode == MODE_BRING else i.ITEM)
+        else:
+            action = self._choose()
+        # Translate the action_object to an integer to have the same output as any other model
+        try:
+            action_obj = next(action_i for action_i, a in enumerate(self.state.actions) if a.name == action)
+        except (StopIteration, UnboundLocalError):
+            print('Will not happen')
+            raise EnvironmentError
+        # noinspection PyUnboundLocalVariable
+        if self.mode == MODE_BRING and len(self._env[i.INVENTORY].by_entity(self.state)):
+            pass
+        elif self.mode == MODE_BRING and not len(self._env[i.INVENTORY].by_entity(self.state)):
+            self.mode = MODE_GET
+        elif self.mode == MODE_GET and len(self._env[i.INVENTORY].by_entity(self.state)) > inventory_size:
+            self.mode = MODE_BRING
+        else:
+            pass
+        return action_obj
+
+    def _choose(self):
+        target = i.DROP_OFF if self.mode == MODE_BRING else i.ITEM
+        if len(self._env.state[i.ITEM]) >= 1:
+            action = self._predict_move(target)
+
+        elif len(self._env[i.INVENTORY].by_entity(self.state)):
+            self.mode = MODE_BRING
+            action = self._predict_move(target)
+        else:
+            action = int(np.random.randint(self._env.action_space.n))
+        # noinspection PyUnboundLocalVariable
+        return action