initial n steps

marl_factory_grid/configs/eight_puzzle.yaml

@@ -35,24 +35,17 @@ Entities:
     # We need a special spawn rule...
     spawnrule:
       # ...which assigns the destinations per agent
-      SpawnDestinationsPerAgent:
-        # we use this parameter
-        coords_or_quantity:
-          # to enable and assign special positions per agent
-          Wolfgang: 1
-          Karl-Heinz: 1
-          Kevin: 1
-          Juergen: 1
-          Soeren: 1
-          Walter: 1
-          Siggi: 1
-          Dennis: 1
+      SpawnDestinationOnAgent: {}
 
 Rules:
   # Utilities
   WatchCollisions:
     done_at_collisions: false
 
+  # Initial random walk
+  DoRandomInitialSteps:
+    random_steps: 10
+
   # Done Conditions
   DoneAtDestinationReach:
     condition: simultanious

marl_factory_grid/environment/factory.py

@@ -136,6 +136,7 @@ class Factory(gym.Env):
 
         # All is set up, trigger entity spawn with variable pos
         self.state.rules.do_all_reset(self.state)
+        self.state.rules.do_all_post_spawn_reset(self.state)
 
         # Build initial observations for all agents
         self.obs_builder.reset(self.state)

marl_factory_grid/environment/groups/global_entities.py

@@ -4,15 +4,17 @@ from random import shuffle
 from typing import Dict
 
 from marl_factory_grid.environment.groups.objects import Objects
-from marl_factory_grid.utils.helpers import POS_MASK
+from marl_factory_grid.utils.helpers import POS_MASK_8, POS_MASK_4
 
 
 class Entities(Objects):
     _entity = Objects
 
-    @staticmethod
-    def neighboring_positions(pos):
-        return [tuple(x) for x in (POS_MASK + pos).reshape(-1, 2)]
+    def neighboring_positions(self, pos):
+        return [tuple(x) for x in (POS_MASK_8 + pos).reshape(-1, 2) if tuple(x) in self._floor_positions]
+
+    def neighboring_4_positions(self, pos):
+        return [tuple(x) for x in (POS_MASK_4 + pos) if tuple(x) in self._floor_positions]
 
     def get_entities_near_pos(self, pos):
         return [y for x in itemgetter(*self.neighboring_positions(pos))(self.pos_dict) for y in x]

marl_factory_grid/environment/rules.py

@@ -1,7 +1,10 @@
 import abc
+import random
 from random import shuffle
 from typing import List, Collection
 
+import numpy as np
+
 from marl_factory_grid.environment import rewards as r, constants as c
 from marl_factory_grid.environment.entity.agent import Agent
 from marl_factory_grid.utils import helpers as h
@@ -37,6 +40,15 @@ class Rule(abc.ABC):
         TODO
 
 
+        :return:
+        """
+        return []
+
+    def on_reset_post_spawn(self, state) -> List[TickResult]:
+        """
+        TODO
+
+
         :return:
         """
         return []
@@ -230,3 +242,33 @@ class WatchCollisions(Rule):
             if inter_entity_collision_detected or collision_in_step:
                 return [DoneResult(validity=c.VALID, identifier=c.COLLISION, reward=self.reward_at_done)]
         return []
+
+
+class DoRandomInitialSteps(Rule):
+    def __init__(self, random_steps: 10):
+        """
+        Special rule which spawns destinations, that are bound to a single agent a fixed set of positions.
+        Useful for introducing specialists, etc. ..
+
+        !!! This rule does not introduce any reward or done condition.
+
+        :param random_steps:  Number of random steps agents perform in an environment.
+                                Useful in the `N-Puzzle` configuration.
+        """
+        super().__init__()
+        self.random_steps = random_steps
+
+    def on_reset_post_spawn(self, state):
+        state.print("Random Initial Steps initiated....")
+        for _ in range(self.random_steps):
+            # Find free positions
+            free_pos = state.random_free_position
+            neighbor_positions = state.entities.neighboring_4_positions(free_pos)
+            random.shuffle(neighbor_positions)
+            chosen_agent = h.get_first(state[c.AGENT].by_pos(neighbor_positions.pop()))
+            assert isinstance(chosen_agent, Agent)
+            valid = chosen_agent.move(free_pos, state)
+            valid_str = " not" if not valid else ""
+            state.print(f"Move {chosen_agent.name} from {chosen_agent.last_pos} "
+                        f"to {chosen_agent.pos} was{valid_str} valid.")
+        pass

marl_factory_grid/modules/destinations/rules.py

@@ -105,10 +105,10 @@ class SpawnDestinationsPerAgent(Rule):
 
         !!! This rule does not introduce any reward or done condition.
 
-        :param coords_or_quantity: Please provide a dictionary with agent names as keys; and a list of possible
-                                     destination coords as value. Example: {Wolfgang: [(0, 0), (1, 1), ...]}
+        :param coords_or_quantity:  Please provide a dictionary with agent names as keys; and a list of possible
+                                        destination coords as value. Example: {Wolfgang: [(0, 0), (1, 1), ...]}
         """
-        super(Rule, self).__init__()
+        super().__init__()
         self.per_agent_positions = dict()
         for agent_name, value in coords_or_quantity.items():
             if isinstance(value, int):
@@ -143,3 +143,25 @@ class SpawnDestinationsPerAgent(Rule):
                     continue
             state[d.DESTINATION].add_item(destination)
         pass
+
+
+class SpawnDestinationOnAgent(Rule):
+    def __init__(self):
+        """
+        Special rule which spawns a single destination bound to a single agent just `below` him. Usefull for
+        the `N-Puzzle` configurations.
+
+        !!! This rule does not introduce any reward or done condition.
+
+        :param coords_or_quantity:  Please provide a dictionary with agent names as keys; and a list of possible
+                                        destination coords as value. Example: {Wolfgang: [(0, 0), (1, 1), ...]}
+        """
+        super().__init__()
+
+    def on_reset(self, state: Gamestate):
+        state.print("Spawn Desitnations")
+        for agent in state[c.AGENT]:
+            destination = Destination(agent.pos, bind_to=agent)
+            state[d.DESTINATION].add_item(destination)
+            assert len(state[d.DESTINATION].by_pos(agent.pos)) == 1
+        pass

marl_factory_grid/utils/helpers.py

@@ -27,9 +27,11 @@ IGNORED_DF_COLUMNS = ['Episode', 'Run',  # For plotting, which values are ignore
                       'train_step', 'step', 'index', 'dirt_amount', 'dirty_pos_count', 'terminal_observation',
                       'episode']
 
-POS_MASK = np.asarray([[[-1, -1], [0, -1], [1, -1]],
-                       [[-1, 0], [0, 0], [1, 0]],
-                       [[-1, 1], [0, 1], [1, 1]]])
+POS_MASK_8 = np.asarray([[[-1, -1], [0, -1], [1, -1]],
+                         [[-1, 0],  [0, 0],  [1, 0]],
+                         [[-1, 1],  [0, 1],  [1, 1]]])
+
+POS_MASK_4 = np.asarray([[0, -1], [-1, 0], [1, 0], [-1, 1], [0, 1], [1, 1]])
 
 MOVEMAP = defaultdict(lambda: (0, 0),
                       {c.NORTH: (-1, 0), c.NORTHEAST: (-1, 1),

marl_factory_grid/utils/states.py

@@ -47,6 +47,12 @@ class StepRules:
                 state.print(rule_reset_printline)
         return c.VALID
 
+    def do_all_post_spawn_reset(self, state):
+        for rule in self.rules:
+            if rule_reset_printline := rule.on_reset_post_spawn(state):
+                state.print(rule_reset_printline)
+        return c.VALID
+
     def tick_step_all(self, state):
         results = list()
         for rule in self.rules:

random_testrun.py

@@ -26,10 +26,10 @@ if __name__ == '__main__':
 
     if explain_config:
         ce = ConfigExplainer()
-        ce.save_all(run_path / 'all_out.yaml')
+        ce.save_all(run_path / 'all_available_configs.yaml')
 
     # Path to config File
-    path = Path('marl_factory_grid/configs/clean_and_bring.yaml')
+    path = Path('marl_factory_grid/configs/eight_puzzle.yaml')
 
     # Env Init
     factory = Factory(path)