Destinations implemented and debugged

algorithms/TSP_dirt_agent.py

@@ -3,27 +3,28 @@ import numpy as np
 from networkx.algorithms.approximation import traveling_salesman as tsp
 
 from environments.factory.base.objects import Agent
-from environments.factory.base.registers import FloorTiles, Actions
 from environments.helpers import points_to_graph
 from environments import helpers as h
+from environments.helpers import Constants as c
 
 
+future_planning = 7
+
 class TSPDirtAgent(Agent):
 
-    def __init__(self, floortiles: FloorTiles, dirt_register, actions: Actions, *args,
+    def __init__(self, env, *args,
                  static_problem: bool = True, **kwargs):
         super().__init__(*args, **kwargs)
         self.static_problem = static_problem
-        self._floortiles = floortiles
-        self._actions = actions
-        self._dirt_register = dirt_register
-        self._floortile_graph = points_to_graph(self._floortiles.positions,
-                                                allow_euclidean_connections=self._actions.allow_diagonal_movement,
-                                                allow_manhattan_connections=self._actions.allow_square_movement)
+        self.local_optimization = True
+        self._env = env
+        self._floortile_graph = points_to_graph(self._env[c.FLOOR].positions,
+                                                allow_euclidean_connections=self._env._actions.allow_diagonal_movement,
+                                                allow_manhattan_connections=self._env._actions.allow_square_movement)
         self._static_route = None
 
     def predict(self, *_, **__):
-        if self._dirt_register.by_pos(self.pos) is not None:
+        if self._env[c.DIRT].by_pos(self.pos) is not None:
             # Translate the action_object to an integer to have the same output as any other model
             action = h.EnvActions.CLEAN_UP
         elif any('door' in x.name.lower() for x in self.tile.guests):
@@ -36,31 +37,50 @@ class TSPDirtAgent(Agent):
         else:
             action = self._predict_move()
         # Translate the action_object to an integer to have the same output as any other model
-        action_obj = next(action_i for action_i, action_obj in enumerate(self._actions) if action_obj == action)
+        action_obj = next(action_i for action_i, action_obj in enumerate(self._env._actions) if action_obj == action)
         return action_obj
 
     def _predict_move(self):
-        if self.static_problem:
-            if self._static_route is None:
-                self._static_route = self.calculate_tsp_route()
-            else:
-                pass
-            next_pos = self._static_route.pop(0)
-            while next_pos == self.pos:
+        if len(self._env[c.DIRT]) >= 1:
+            if self.static_problem:
+                if not self._static_route:
+                    self._static_route = self.calculate_tsp_route()
+                else:
+                    pass
                 next_pos = self._static_route.pop(0)
-        else:
-            raise NotImplementedError
+                while next_pos == self.pos:
+                    next_pos = self._static_route.pop(0)
+            else:
+                if not self._static_route:
+                    self._static_route = self.calculate_tsp_route()[:7]
+                next_pos = self._static_route.pop(0)
+                while next_pos == self.pos:
+                    next_pos = self._static_route.pop(0)
 
-        diff = np.subtract(next_pos, self.pos)
-        # Retrieve action based on the pos dif (like in: What do i have to do to get there?)
-        try:
-            action = next(action for action, pos_diff in h.ACTIONMAP.items()
-                          if (diff == pos_diff).all())
-        except StopIteration:
-            print('This Should not happen!')
+            diff = np.subtract(next_pos, self.pos)
+            # Retrieve action based on the pos dif (like in: What do i have to do to get there?)
+            try:
+                action = next(action for action, pos_diff in h.ACTIONMAP.items()
+                              if (diff == pos_diff).all())
+            except StopIteration:
+                print('This Should not happen!')
+        else:
+            action = int(np.random.randint(self._env.action_space.n))
         return action
 
     def calculate_tsp_route(self):
+        if self.local_optimization:
+            nodes = \
+                [self.pos] + \
+                [x for x in self._env[c.DIRT].positions if max(abs(np.subtract(x, self.pos))) < 3]
+            try:
+                while len(nodes) < 7:
+                    nodes += [next(x for x in self._env[c.DIRT].positions if x not in nodes)]
+            except StopIteration:
+                nodes = [self.pos] + self._env[c.DIRT].positions
+
+        else:
+            nodes = [self.pos] + self._env[c.DIRT].positions
         route = tsp.traveling_salesman_problem(self._floortile_graph,
-                                               nodes=[self.pos] + [x for x in self._dirt_register.positions])
+                                               nodes=nodes, cycle=True, method=tsp.greedy_tsp)
         return route

environments/factory/base/base_factory.py

@@ -64,7 +64,7 @@ class BaseFactory(gym.Env):
     def __init__(self, level_name='simple', n_agents=1, max_steps=int(5e2),
                  mv_prop: MovementProperties = MovementProperties(),
                  obs_prop: ObservationProperties = ObservationProperties(),
-                 parse_doors=False, done_at_collision=False,
+                 parse_doors=False, done_at_collision=False, inject_agents: Union[None, List] = None,
                  verbose=False, doors_have_area=True, env_seed=time.time_ns(), individual_rewards=False,
                  **kwargs):
 
@@ -98,6 +98,7 @@ class BaseFactory(gym.Env):
         self.done_at_collision = done_at_collision
         self._record_episodes = False
         self.parse_doors = parse_doors
+        self._injected_agents = inject_agents or []
         self.doors_have_area = doors_have_area
         self.individual_rewards = individual_rewards
 
@@ -108,8 +109,10 @@ class BaseFactory(gym.Env):
         return self._entities[item]
 
     def _base_init_env(self):
+
+        # All entities
         # Objects
-        entities = {}
+        self._entities = Entities()
         # Level
         level_filepath = Path(__file__).parent.parent / h.LEVELS_DIR / f'{self.level_name}.txt'
         parsed_level = h.parse_level(level_filepath)
@@ -121,14 +124,14 @@ class BaseFactory(gym.Env):
             np.argwhere(level_array == c.OCCUPIED_CELL.value),
             self._level_shape
         )
-        entities.update({c.WALLS: walls})
+        self._entities.register_additional_items({c.WALLS: walls})
 
         # Floor
         floor = FloorTiles.from_argwhere_coordinates(
             np.argwhere(level_array == c.FREE_CELL.value),
             self._level_shape
         )
-        entities.update({c.FLOOR: floor})
+        self._entities.register_additional_items({c.FLOOR: floor})
 
         # NOPOS
         self._NO_POS_TILE = Tile(c.NO_POS.value)
@@ -141,7 +144,7 @@ class BaseFactory(gym.Env):
                 doors = Doors.from_tiles(door_tiles, self._level_shape,
                                          entity_kwargs=dict(context=floor)
                                          )
-                entities.update({c.DOORS: doors})
+                self._entities.register_additional_items({c.DOORS: doors})
 
         # Actions
         self._actions = Actions(self.mv_prop, can_use_doors=self.parse_doors)
@@ -149,12 +152,22 @@ class BaseFactory(gym.Env):
             self._actions.register_additional_items(additional_actions)
 
         # Agents
-        agents = Agents.from_tiles(floor.empty_tiles[:self.n_agents], self._level_shape,
-                                   individual_slices=self.obs_prop.render_agents == a_obs.SEPERATE,
-                                   hide_from_obs_builder=self.obs_prop.render_agents == a_obs.LEVEL,
-                                   is_observable=self.obs_prop.render_agents != a_obs.NOT
-                                   )
-        entities.update({c.AGENT: agents})
+        agents_to_spawn = self.n_agents-len(self._injected_agents)
+        agents_kwargs = dict(level_shape=self._level_shape,
+                             individual_slices=self.obs_prop.render_agents == a_obs.SEPERATE,
+                             hide_from_obs_builder=self.obs_prop.render_agents == a_obs.LEVEL,
+                             is_observable=self.obs_prop.render_agents != a_obs.NOT)
+        if agents_to_spawn:
+            agents = Agents.from_tiles(floor.empty_tiles[:agents_to_spawn], **agents_kwargs)
+        else:
+            agents = Agents(**agents_kwargs)
+        if self._injected_agents:
+            initialized_injections = list()
+            for i, injection in enumerate(self._injected_agents):
+                agents.register_item(injection(self, floor.empty_tiles[agents_to_spawn+i+1], static_problem=False))
+                initialized_injections.append(agents[-1])
+            self._initialized_injections = initialized_injections
+        self._entities.register_additional_items({c.AGENT: agents})
 
         if self.obs_prop.additional_agent_placeholder is not None:
             # TODO: Make this accept Lists for multiple placeholders
@@ -165,11 +178,7 @@ class BaseFactory(gym.Env):
                                                       fill_value=self.obs_prop.additional_agent_placeholder)
                                                   )
 
-            entities.update({c.AGENT_PLACEHOLDER: placeholder})
-
-        # All entities
-        self._entities = Entities()
-        self._entities.register_additional_items(entities)
+            self._entities.register_additional_items({c.AGENT_PLACEHOLDER: placeholder})
 
         # Additional Entitites from SubEnvs
         if additional_entities := self.additional_entities:
@@ -182,6 +191,7 @@ class BaseFactory(gym.Env):
         arrays = self._entities.obs_arrays
 
         obs_cube_z = sum([a.shape[0] if not self[key].is_per_agent else 1 for key, a in arrays.items()])
+        obs_cube_z += 1 if self.obs_prop.show_global_position_info else 0
         self._obs_cube = np.zeros((obs_cube_z, *self._level_shape), dtype=np.float32)
 
     def reset(self) -> (np.ndarray, int, bool, dict):
@@ -279,7 +289,7 @@ class BaseFactory(gym.Env):
         if self.n_agents == 1:
             obs = self._build_per_agent_obs(self[c.AGENT][0], state_array_dict)
         elif self.n_agents >= 2:
-            obs = np.stack(self._build_per_agent_obs(agent, state_array_dict) for agent in self[c.AGENT])
+            obs = np.stack([self._build_per_agent_obs(agent, state_array_dict) for agent in self[c.AGENT]])
         else:
             raise ValueError('n_agents cannot be smaller than 1!!')
         return obs
@@ -384,6 +394,7 @@ class BaseFactory(gym.Env):
 
             if self.obs_prop.pomdp_r:
                 oobs = self._do_pomdp_obs_cutout(agent, other_agent_obs)[0]
+                # noinspection PyUnresolvedReferences
                 mask = (oobs != c.SHADOWED_CELL.value).astype(int)
                 obs[0] += oobs * mask
 
@@ -497,7 +508,7 @@ class BaseFactory(gym.Env):
             if self._actions.is_moving_action(agent.temp_action):
                 if agent.temp_valid:
                     # info_dict.update(movement=1)
-                    per_agent_reward -= 0.01
+                    per_agent_reward -= 0.001
                     pass
                 else:
                     per_agent_reward -= 0.05
@@ -553,6 +564,7 @@ class BaseFactory(gym.Env):
             self.print(f"reward is {reward}")
         return reward, combined_info_dict
 
+    # noinspection PyGlobalUndefined
     def render(self, mode='human'):
         if not self._renderer:  # lazy init
             from environments.factory.base.renderer import Renderer, RenderEntity
@@ -560,6 +572,7 @@ class BaseFactory(gym.Env):
             height, width = self._obs_cube.shape[1:]
             self._renderer = Renderer(width, height, view_radius=self._pomdp_r, fps=5)
 
+        # noinspection PyUnboundLocalVariable
         walls = [RenderEntity('wall', wall.pos) for wall in self[c.WALLS]]
 
         agents = []
@@ -582,6 +595,12 @@ class BaseFactory(gym.Env):
         with filepath.open('w') as f:
             simplejson.dump(d, f, indent=4, namedtuple_as_object=True)
 
+    def get_injected_agents(self) -> list:
+        if hasattr(self, '_initialized_injections'):
+            return self._initialized_injections
+        else:
+            return []
+
     def _summarize_state(self):
         summary = {f'{REC_TAC}step': self._steps}
 
@@ -621,9 +640,15 @@ class BaseFactory(gym.Env):
     def additional_obs_build(self) -> List[np.ndarray]:
         return []
 
-    @abc.abstractmethod
     def additional_per_agent_obs_build(self, agent) -> List[np.ndarray]:
-        return []
+        additional_per_agent_obs = []
+        if self.obs_prop.show_global_position_info:
+            pos_array = np.zeros(self.observation_space.shape[1:])
+            for xy in range(1):
+                pos_array[0, xy] = agent.pos[xy] / self._level_shape[xy]
+            additional_per_agent_obs.append(pos_array)
+
+        return additional_per_agent_obs
 
     @abc.abstractmethod
     def do_additional_reset(self) -> None:

environments/factory/base/registers.py

@@ -50,6 +50,8 @@ class Register:
 
     def __getitem__(self, item):
         if isinstance(item, (int, np.int64, np.int32)):
+            if item < 0:
+                item = len(self._register) - abs(item)
             try:
                 return next(v for i, v in enumerate(self._register.values()) if i == item)
             except StopIteration:
@@ -147,10 +149,10 @@ class MovingEntityObjectRegister(EntityObjectRegister, ABC):
         if self.individual_slices:
             self._array = np.delete(self._array, idx, axis=0)
 
-    def delete_item(self, item):
-        self.delete_item_by_name(item.name)
+    def delete_entity(self, item):
+        self.delete_entity_by_name(item.name)
 
-    def delete_item_by_name(self, name):
+    def delete_entity_by_name(self, name):
         del self[name]
 
 
@@ -320,8 +322,11 @@ class Agents(MovingEntityObjectRegister):
     def positions(self):
         return [agent.pos for agent in self]
 
-    def __setitem__(self, key, value):
-        self._register[self[key].name] = value
+    def replace_agent(self, key, agent):
+        old_agent = self[key]
+        self[key].tile.leave(self[key])
+        agent._name = old_agent.name
+        self._register[agent.name] = agent
 
 
 class Doors(EntityObjectRegister):

environments/factory/factory_destination.py

@@ -0,0 +1,292 @@
+import time
+from collections import defaultdict
+from enum import Enum
+from typing import List, Union, NamedTuple, Dict
+import numpy as np
+import random
+
+from environments.factory.base.base_factory import BaseFactory
+from environments.helpers import Constants as c
+from environments import helpers as h
+from environments.factory.base.objects import Agent, Entity, Action, Tile
+from environments.factory.base.registers import Entities, MovingEntityObjectRegister
+
+from environments.factory.base.renderer import RenderEntity
+
+
+DESTINATION = 1
+DESTINATION_DONE = 0.5
+
+
+class Destination(Entity):
+
+    @property
+    def any_agent_has_dwelled(self):
+        return bool(len(self._per_agent_times))
+
+    @property
+    def currently_dwelling_names(self):
+        return self._per_agent_times.keys()
+
+    @property
+    def can_collide(self):
+        return False
+
+    @property
+    def encoding(self):
+        return DESTINATION
+
+    def __init__(self, *args, dwell_time: int = 0, **kwargs):
+        super(Destination, self).__init__(*args, **kwargs)
+        self.dwell_time = dwell_time
+        self._per_agent_times = defaultdict(lambda: dwell_time)
+
+    def wait(self, agent: Agent):
+        self._per_agent_times[agent.name] -= 1
+        return c.VALID
+
+    def leave(self, agent: Agent):
+        del self._per_agent_times[agent.name]
+
+    @property
+    def is_considered_reached(self):
+        agent_at_position = any(c.AGENT.name.lower() in x.name.lower() for x in self.tile.guests_that_can_collide)
+        return (agent_at_position and not self.dwell_time) or any(x == 0 for x in self._per_agent_times.values())
+
+    def agent_is_dwelling(self, agent: Agent):
+        return self._per_agent_times[agent.name] < self.dwell_time
+
+    def summarize_state(self, n_steps=None) -> dict:
+        state_summary = super().summarize_state(n_steps=n_steps)
+        state_summary.update(per_agent_times=self._per_agent_times)
+        return state_summary
+
+
+class Destinations(MovingEntityObjectRegister):
+
+    _accepted_objects = Destination
+    _light_blocking = False
+
+    def as_array(self):
+        self._array[:] = c.FREE_CELL.value
+        for item in self:
+            if item.pos != c.NO_POS.value:
+                self._array[0, item.x, item.y] = item.encoding
+        return self._array
+
+    def __repr__(self):
+        super(Destinations, self).__repr__()
+
+
+class ReachedDestinations(Destinations):
+    _accepted_objects = Destination
+    _light_blocking = False
+
+    def __init__(self, *args, **kwargs):
+        super(ReachedDestinations, self).__init__(*args, is_observable=False, **kwargs)
+
+    def summarize_states(self, n_steps=None):
+        return {}
+
+
+class DestSpawnMode(object):
+    DONE        = 'DONE'
+    GROUPED     = 'GROUPED'
+    PER_DEST    = 'PER_DEST'
+
+
+class DestinationProperties(NamedTuple):
+    n_dests:                                     int = 1     # How many destinations are there
+    dwell_time:                                  int = 0     # How long does the agent need to "wait" on a destination
+    spawn_frequency:                             int = 0
+    spawn_in_other_zone:                        bool = True  #
+    spawn_mode:                                  str = DestSpawnMode.DONE
+
+    assert dwell_time >= 0, 'dwell_time cannot be < 0!'
+    assert spawn_frequency >= 0, 'spawn_frequency cannot be < 0!'
+    assert n_dests >= 0, 'n_destinations cannot be < 0!'
+    assert (spawn_mode == DestSpawnMode.DONE) != bool(spawn_frequency)
+
+
+# noinspection PyAttributeOutsideInit, PyAbstractClass
+class DestinationFactory(BaseFactory):
+    # noinspection PyMissingConstructor
+
+    def __init__(self, *args, dest_prop: DestinationProperties  = DestinationProperties(),
+                 env_seed=time.time_ns(), **kwargs):
+        if isinstance(dest_prop, dict):
+            dest_prop = DestinationProperties(**dest_prop)
+        self.dest_prop = dest_prop
+        kwargs.update(env_seed=env_seed)
+        self._dest_rng = np.random.default_rng(env_seed)
+        super().__init__(*args, **kwargs)
+
+    @property
+    def additional_actions(self) -> Union[Action, List[Action]]:
+        # noinspection PyUnresolvedReferences
+        super_actions = super().additional_actions
+        if self.dest_prop.dwell_time:
+            super_actions.append(Action(enum_ident=h.EnvActions.WAIT_ON_DEST))
+        return super_actions
+
+    @property
+    def additional_entities(self) -> Dict[(Enum, Entities)]:
+        # noinspection PyUnresolvedReferences
+        super_entities = super().additional_entities
+
+        empty_tiles = self[c.FLOOR].empty_tiles[:self.dest_prop.n_dests]
+        destinations = Destinations.from_tiles(
+            empty_tiles, self._level_shape,
+            entity_kwargs=dict(
+                dwell_time=self.dest_prop.dwell_time)
+        )
+        reached_destinations = ReachedDestinations(level_shape=self._level_shape)
+
+        super_entities.update({c.DESTINATION: destinations, c.REACHEDDESTINATION: reached_destinations})
+        return super_entities
+
+    def additional_per_agent_obs_build(self, agent) -> List[np.ndarray]:
+        additional_per_agent_obs_build = super().additional_per_agent_obs_build(agent)
+        return additional_per_agent_obs_build
+
+    def wait(self, agent: Agent):
+        if destiantion := self[c.DESTINATION].by_pos(agent.pos):
+            valid = destiantion.wait(agent)
+            return valid
+        else:
+            return c.NOT_VALID
+
+    def do_additional_actions(self, agent: Agent, action: Action) -> Union[None, c]:
+        # noinspection PyUnresolvedReferences
+        valid = super().do_additional_actions(agent, action)
+        if valid is None:
+            if action == h.EnvActions.WAIT_ON_DEST:
+                valid = self.wait(agent)
+                return valid
+            else:
+                return None
+        else:
+            return valid
+
+    def do_additional_reset(self) -> None:
+        # noinspection PyUnresolvedReferences
+        super().do_additional_reset()
+        self._dest_spawn_timer = dict()
+
+    def trigger_destination_spawn(self):
+        destinations_to_spawn = [key for key, val in self._dest_spawn_timer.items()
+                                 if val == self.dest_prop.spawn_frequency]
+        if destinations_to_spawn:
+            n_dest_to_spawn = len(destinations_to_spawn)
+            if self.dest_prop.spawn_mode != DestSpawnMode.GROUPED:
+                destinations = [Destination(tile) for tile in self[c.FLOOR].empty_tiles[:n_dest_to_spawn]]
+                self[c.DESTINATION].register_additional_items(destinations)
+                for dest in destinations_to_spawn:
+                    del self._dest_spawn_timer[dest]
+                self.print(f'{n_dest_to_spawn} new destinations have been spawned')
+            elif self.dest_prop.spawn_mode == DestSpawnMode.GROUPED and n_dest_to_spawn == self.dest_prop.n_dests:
+                destinations = [Destination(tile) for tile in self[c.FLOOR].empty_tiles[:n_dest_to_spawn]]
+                self[c.DESTINATION].register_additional_items(destinations)
+                for dest in destinations_to_spawn:
+                    del self._dest_spawn_timer[dest]
+                self.print(f'{n_dest_to_spawn} new destinations have been spawned')
+            else:
+                self.print(f'{n_dest_to_spawn} new destinations could be spawned, but waiting for all.')
+                pass
+        else:
+            self.print('No Items are spawning, limit is reached.')
+
+    def do_additional_step(self) -> dict:
+        # noinspection PyUnresolvedReferences
+        info_dict = super().do_additional_step()
+        for key, val in self._dest_spawn_timer.items():
+            self._dest_spawn_timer[key] = min(self.dest_prop.spawn_frequency, self._dest_spawn_timer[key] + 1)
+        for dest in list(self[c.DESTINATION].values()):
+            if dest.is_considered_reached:
+                self[c.REACHEDDESTINATION].register_item(dest)
+                self[c.DESTINATION].delete_entity(dest)
+                self._dest_spawn_timer[dest.name] = 0
+                self.print(f'{dest.name} is reached now, removing...')
+            else:
+                for agent_name in dest.currently_dwelling_names:
+                    agent = self[c.AGENT].by_name(agent_name)
+                    if agent.pos == dest.pos:
+                        self.print(f'{agent.name} is still waiting.')
+                        pass
+                    else:
+                        dest.leave(agent)
+                        self.print(f'{agent.name} left the destination early.')
+        self.trigger_destination_spawn()
+        return info_dict
+
+    def calculate_additional_reward(self, agent: Agent) -> (int, dict):
+        # noinspection PyUnresolvedReferences
+        reward, info_dict = super().calculate_additional_reward(agent)
+        if h.EnvActions.WAIT_ON_DEST == agent.temp_action:
+            if agent.temp_valid:
+                info_dict.update({f'{agent.name}_waiting_at_dest': 1})
+                info_dict.update(agent_waiting_at_dest=1)
+                self.print(f'{agent.name} just waited at {agent.pos}')
+                reward += 0.1
+            else:
+                info_dict.update({f'{agent.name}_tried_failed': 1})
+                info_dict.update(agent_waiting_failed=1)
+                self.print(f'{agent.name} just tried to wait wait at {agent.pos} but failed')
+                reward -= 0.1
+        if len(self[c.REACHEDDESTINATION]):
+            for reached_dest in list(self[c.REACHEDDESTINATION]):
+                if agent.pos == reached_dest.pos:
+                    info_dict.update({f'{agent.name}_reached_destination': 1})
+                    info_dict.update(agent_reached_destination=1)
+                    self.print(f'{agent.name} just reached destination at {agent.pos}')
+                    reward += 0.5
+                    self[c.REACHEDDESTINATION].delete_entity(reached_dest)
+        return reward, info_dict
+
+    def render_additional_assets(self, mode='human'):
+        # noinspection PyUnresolvedReferences
+        additional_assets = super().render_additional_assets()
+        destinations = [RenderEntity(c.DESTINATION.value, dest.pos) for dest in self[c.DESTINATION]]
+        additional_assets.extend(destinations)
+        return additional_assets
+
+
+if __name__ == '__main__':
+    from environments.utility_classes import AgentRenderOptions as ARO, ObservationProperties
+
+    render = True
+
+    dest_probs = DestinationProperties(n_dests=2, spawn_frequency=5, spawn_mode=DestSpawnMode.GROUPED)
+
+    obs_props = ObservationProperties(render_agents=ARO.LEVEL, omit_agent_self=True, pomdp_r=2)
+
+    move_props = {'allow_square_movement': True,
+                  'allow_diagonal_movement': False,
+                  'allow_no_op': False}
+
+    factory = DestinationFactory(n_agents=10, done_at_collision=False,
+                                 level_name='rooms', max_steps=400,
+                                 obs_prop=obs_props, parse_doors=True,
+                                 verbose=True,
+                                 mv_prop=move_props, dest_prop=dest_probs
+                                 )
+
+    # noinspection DuplicatedCode
+    n_actions = factory.action_space.n - 1
+    _ = factory.observation_space
+
+    for epoch in range(4):
+        random_actions = [[random.randint(0, n_actions) for _
+                           in range(factory.n_agents)] for _
+                          in range(factory.max_steps + 1)]
+        env_state = factory.reset()
+        r = 0
+        for agent_i_action in random_actions:
+            env_state, step_r, done_bool, info_obj = factory.step(agent_i_action)
+            r += step_r
+            if render:
+                factory.render()
+            if done_bool:
+                break
+        print(f'Factory run {epoch} done, reward is:\n    {r}')
+    pass

environments/factory/factory_dirt.py

@@ -66,7 +66,7 @@ class DirtRegister(MovingEntityObjectRegister):
             self._array[:] = c.FREE_CELL.value
             for dirt in list(self.values()):
                 if dirt.amount == 0:
-                    self.delete_item(dirt)
+                    self.delete_entity(dirt)
                 self._array[0, dirt.x, dirt.y] = dirt.amount
         else:
             self._array = np.zeros((1, *self._level_shape))
@@ -155,7 +155,7 @@ class DirtFactory(BaseFactory):
             new_dirt_amount = dirt.amount - self.dirt_prop.clean_amount
 
             if new_dirt_amount <= 0:
-                self[c.DIRT].delete_item(dirt)
+                self[c.DIRT].delete_entity(dirt)
             else:
                 dirt.set_new_amount(max(new_dirt_amount, c.FREE_CELL.value))
             return c.VALID
@@ -243,11 +243,12 @@ class DirtFactory(BaseFactory):
                 # Reward if pickup succeds,
                 #  0.5 on every pickup
                 reward += 0.5
+                info_dict.update(dirt_cleaned=1)
                 if self.dirt_prop.done_when_clean and (len(self[c.DIRT]) == 0):
                     #  0.5 additional reward for the very last pickup
-                    reward += 0.5
+                    reward += 4.5
+                    info_dict.update(done_clean=1)
                 self.print(f'{agent.name} did just clean up some dirt at {agent.pos}.')
-                info_dict.update(dirt_cleaned=1)
             else:
                 reward -= 0.01
                 self.print(f'{agent.name} just tried to clean up some dirt at {agent.pos}, but failed.')
@@ -288,23 +289,22 @@ if __name__ == '__main__':
                           doors_have_area=False,
                           obs_prop=obs_props, parse_doors=True,
                           record_episodes=True, verbose=True,
-                          mv_prop=move_props, dirt_prop=dirt_props
+                          mv_prop=move_props, dirt_prop=dirt_props,
+                          inject_agents=[TSPDirtAgent]
                           )
 
     # noinspection DuplicatedCode
     n_actions = factory.action_space.n - 1
     _ = factory.observation_space
 
-    for epoch in range(4):
+    for epoch in range(10):
         random_actions = [[random.randint(0, n_actions) for _
                            in range(factory.n_agents)] for _
                           in range(factory.max_steps+1)]
         env_state = factory.reset()
         if render:
             factory.render()
-        random_start_position = factory[c.AGENT][0].tile
-        factory[c.AGENT][0] = tsp_agent = TSPDirtAgent(factory[c.FLOOR], factory[c.DIRT],
-                                                       factory._actions, random_start_position)
+        tsp_agent = factory.get_injected_agents()[0]
 
         r = 0
         for agent_i_action in random_actions:

environments/factory/factory_item.py

@@ -308,7 +308,7 @@ class ItemFactory(BaseFactory):
             if item.auto_despawn >= 1:
                 item.set_auto_despawn(item.auto_despawn-1)
             elif not item.auto_despawn:
-                self[c.ITEM].delete_item(item)
+                self[c.ITEM].delete_entity(item)
             else:
                 pass
 

environments/helpers.py

@@ -55,6 +55,10 @@ class Constants(Enum):
     CHARGE_POD          = 'Charge_Pod'
     BATTERIES           = 'BATTERIES'
 
+    # Destination Env
+    DESTINATION         = 'Destination'
+    REACHEDDESTINATION  = 'ReachedDestination'
+
     def __bool__(self):
         if 'not_' in self.value:
             return False
@@ -86,11 +90,12 @@ class MovingAction(Enum):
 
 
 class EnvActions(Enum):
-    NOOP        = 'no_op'
-    USE_DOOR    = 'use_door'
-    CLEAN_UP    = 'clean_up'
-    ITEM_ACTION = 'item_action'
-    CHARGE      = 'charge'
+    NOOP            = 'no_op'
+    USE_DOOR        = 'use_door'
+    CLEAN_UP        = 'clean_up'
+    ITEM_ACTION     = 'item_action'
+    CHARGE          = 'charge'
+    WAIT_ON_DEST    = 'wait'
 
 
 m = MovingAction

environments/utility_classes.py

@@ -23,6 +23,7 @@ class ObservationProperties(NamedTuple):
     cast_shadows = True
     frames_to_stack: int = 0
     pomdp_r: int = 0
+    show_global_position_info: bool = True
 
 
 class MarlFrameStack(gym.ObservationWrapper):

plotting/plotting.py

@@ -31,6 +31,8 @@ def prepare_tex(df, hue, style, hue_order):
     lineplot = sns.lineplot(data=df, x='Episode', y='Score', ci=95, palette=PALETTE,
                             hue_order=hue_order, hue=hue, style=style)
     # lineplot.set_title(f'{sorted(list(df["Measurement"].unique()))}')
+    plt.legend(bbox_to_anchor=(1.02, 1), loc='upper left', borderaxespad=0)
+    plt.tight_layout()
     return lineplot
 
 

reload_agent.py

@@ -20,8 +20,8 @@ if __name__ == '__main__':
     render = True
     record = True
     seed = 67
-    n_agents = 2
-    out_path = Path('study_out/e_1_obs_stack_3_gae_0.25_n_steps_16/seperate_N/dirt/A2C_obs_stack_3_gae_0.25_n_steps_16/0_A2C_obs_stack_3_gae_0.25_n_steps_16')
+    n_agents = 1
+    out_path = Path('study_out/e_1_new_reward/no_obs/dirt/A2C_new_reward/0_A2C_new_reward')
     out_path_2 = Path('study_out/e_1_obs_stack_3_gae_0.25_n_steps_16/seperate_N/dirt/A2C_obs_stack_3_gae_0.25_n_steps_16/1_A2C_obs_stack_3_gae_0.25_n_steps_16')
     model_path = out_path
 
@@ -38,7 +38,7 @@ if __name__ == '__main__':
     other_model = out_path / 'model.zip'
 
     model_cls = next(val for key, val in h.MODEL_MAP.items() if key in out_path.parent.name)
-    models = [model_cls.load(this_model), model_cls.load(other_model)]
+    models = [model_cls.load(this_model)]  # , model_cls.load(other_model)]
 
     # Init Env
     with DirtFactory(**env_kwargs) as env:
@@ -61,5 +61,5 @@ if __name__ == '__main__':
                     env.render()
                 if done_bool:
                     break
-                print(f'Factory run {episode} done, reward is:\n    {rew}')
+            print(f'Factory run {episode} done, reward is:\n    {rew}')
     print('all done')

studies/e_1.py

@@ -75,7 +75,7 @@ baseline_monitor_file = 'e_1_baseline'
 from stable_baselines3 import A2C
 
 def policy_model_kwargs():
-    return dict(gae_lambda=0.25, n_steps=16, max_grad_norm=0, use_rms_prop=True)
+    return dict() # gae_lambda=0.25, n_steps=16, max_grad_norm=0.25, use_rms_prop=True)
 
 
 def dqn_model_kwargs():
@@ -198,12 +198,12 @@ if __name__ == '__main__':
     ood_run = True
     plotting = True
 
-    train_steps = 5e6
+    train_steps = 1e7
     n_seeds = 3
     frames_to_stack = 3
 
     # Define a global studi save path
-    start_time = 'rms_weight_decay_0'  # int(time.time())
+    start_time = 'new_reward'  # int(time.time())
     study_root_path = Path(__file__).parent.parent / 'study_out' / f'{Path(__file__).stem}_{start_time}'
 
     # Define Global Env Parameters
@@ -516,7 +516,7 @@ if __name__ == '__main__':
                 # df_melted["Measurements"] = df_melted["Measurement"] + " " + df_melted["monitor"]
 
                 # Plotting
-                # fig, ax = plt.subplots(figsize=(11.7, 8.27))
+                fig, ax = plt.subplots(figsize=(11.7, 8.27))
 
                 c = sns.catplot(data=df_melted[df_melted['env'] == env_name],
                                 x='Measurement', hue='monitor', row='model', col='obs_mode', y='Score',
@@ -525,7 +525,7 @@ if __name__ == '__main__':
                 c.set_xticklabels(rotation=65, horizontalalignment='right')
                 # c.fig.subplots_adjust(top=0.9)  # adjust the Figure in rp
                 c.fig.suptitle(f"Cat plot for {env_name}")
-                # plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)
+                plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)
                 plt.tight_layout()
                 plt.savefig(study_root_path / f'results_{n_agents}_agents_{env_name}.png')
         pass