Experiments look good

environments/factory/factory_dirt_stationary_machines.py

@@ -0,0 +1,58 @@
+from typing import Dict, List, Union
+
+import numpy as np
+
+from environments.factory.base.objects import Agent, Entity, Action
+from environments.factory.factory_dirt import Dirt, DirtRegister, DirtFactory
+from environments.factory.base.objects import Floor
+from environments.factory.base.registers import Floors, Entities, EntityRegister
+
+
+class Machines(EntityRegister):
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+
+class Machine(Entity):
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+
+class StationaryMachinesDirtFactory(DirtFactory):
+
+    def __init__(self, *args, **kwargs):
+        self._machine_coords = [(6, 6), (12, 13)]
+        super().__init__(*args, **kwargs)
+
+    def entities_hook(self) -> Dict[(str, Entities)]:
+        super_entities = super().entities_hook()
+
+        return super_entities
+
+    def reset_hook(self) -> None:
+                pass
+
+    def observations_hook(self) -> Dict[str, np.typing.ArrayLike]:
+        pass
+
+    def actions_hook(self) -> Union[Action, List[Action]]:
+        pass
+
+    def step_hook(self) -> (List[dict], dict):
+
+        pass
+
+    def per_agent_raw_observations_hook(self, agent) -> Dict[str, np.typing.ArrayLike]:
+        super_per_agent_raw_observations = super().per_agent_raw_observations_hook(agent)
+        return super_per_agent_raw_observations
+
+    def per_agent_reward_hook(self, agent: Agent) -> Dict[str, dict]:
+        pass
+
+    def pre_step_hook(self) -> None:
+        pass
+
+    def post_step_hook(self) -> dict:
+        pass