Adjustments and Documentation, recording and new environments, refactoring

environments/factory/__init__.py

@@ -2,8 +2,11 @@ def make(env_name, pomdp_r=2, max_steps=400, stack_n_frames=3, n_agents=1, indiv
     import yaml
     from pathlib import Path
     from environments.factory.combined_factories import DirtItemFactory
-    from environments.factory.factory_item import ItemFactory, ItemProperties
+    from environments.factory.factory_item import ItemFactory
-    from environments.factory.factory_dirt import DirtProperties, DirtFactory, RewardsDirt
+    from environments.factory.additional.item.item_util import ItemProperties
+    from environments.factory.factory_dirt import DirtFactory
+    from environments.factory.dirt_util import DirtProperties
+    from environments.factory.dirt_util import RewardsDirt
     from environments.utility_classes import AgentRenderOptions
 
     with (Path(__file__).parent / 'levels' / 'parameters' / f'{env_name}.yaml').open('r') as stream:

environments/factory/additional/btry/btry_collections.py

@@ -0,0 +1,50 @@
+from environments.factory.additional.btry.btry_objects import Battery, ChargePod
+from environments.factory.base.registers import EnvObjectCollection, EntityCollection
+
+
+class BatteriesRegister(EnvObjectCollection):
+
+    _accepted_objects = Battery
+
+    def __init__(self, *args, **kwargs):
+        super(BatteriesRegister, self).__init__(*args, individual_slices=True,
+                                                is_blocking_light=False, can_be_shadowed=False, **kwargs)
+        self.is_observable = True
+
+    def spawn_batteries(self, agents, initial_charge_level):
+        batteries = [self._accepted_objects(initial_charge_level, agent, self) for _, agent in enumerate(agents)]
+        self.add_additional_items(batteries)
+
+    def summarize_states(self, n_steps=None):
+        # as dict with additional nesting
+        # return dict(items=super(Inventories, cls).summarize_states())
+        return super(BatteriesRegister, self).summarize_states(n_steps=n_steps)
+
+    # Todo Move this to Mixin!
+    def by_entity(self, entity):
+        try:
+            return next((x for x in self if x.belongs_to_entity(entity)))
+        except StopIteration:
+            return None
+
+    def idx_by_entity(self, entity):
+        try:
+            return next((idx for idx, x in enumerate(self) if x.belongs_to_entity(entity)))
+        except StopIteration:
+            return None
+
+    def as_array_by_entity(self, entity):
+        return self._array[self.idx_by_entity(entity)]
+
+
+class ChargePods(EntityCollection):
+
+    _accepted_objects = ChargePod
+
+    def __repr__(self):
+        super(ChargePods, self).__repr__()
+
+    def summarize_states(self, n_steps=None):
+        # as dict with additional nesting
+        # return dict(items=super(Inventories, cls).summarize_states())
+        return super(ChargePods, self).summarize_states(n_steps=n_steps)

environments/factory/additional/btry/btry_objects.py

@@ -0,0 +1,67 @@
+from environments import helpers as h
+from environments.factory.base.objects import BoundingMixin, EnvObject, Entity
+from environments.factory.additional.btry.btry_util import Constants as c
+
+
+class Battery(BoundingMixin, EnvObject):
+
+    @property
+    def is_discharged(self):
+        return self.charge_level == 0
+
+    def __init__(self, initial_charge_level: float, *args, **kwargs):
+        super(Battery, self).__init__(*args, **kwargs)
+        self.charge_level = initial_charge_level
+
+    def encoding(self):
+        return self.charge_level
+
+    def do_charge_action(self, amount):
+        if self.charge_level < 1:
+            # noinspection PyTypeChecker
+            self.charge_level = min(1, amount + self.charge_level)
+            return c.VALID
+        else:
+            return c.NOT_VALID
+
+    def decharge(self, amount) -> c:
+        if self.charge_level != 0:
+            # noinspection PyTypeChecker
+            self.charge_level = max(0, amount + self.charge_level)
+            self._collection.notify_change_to_value(self)
+            return c.VALID
+        else:
+            return c.NOT_VALID
+
+    def summarize_state(self, **_):
+        attr_dict = {key: str(val) for key, val in self.__dict__.items() if not key.startswith('_') and key != 'data'}
+        attr_dict.update(dict(name=self.name))
+        return attr_dict
+
+
+class ChargePod(Entity):
+
+    @property
+    def encoding(self):
+        return c.CHARGE_POD
+
+    def __init__(self, *args, charge_rate: float = 0.4,
+                 multi_charge: bool = False, **kwargs):
+        super(ChargePod, self).__init__(*args, **kwargs)
+        self.charge_rate = charge_rate
+        self.multi_charge = multi_charge
+
+    def charge_battery(self, battery: Battery):
+        if battery.charge_level == 1.0:
+            return c.NOT_VALID
+        if sum(guest for guest in self.tile.guests if 'agent' in guest.name.lower()) > 1:
+            return c.NOT_VALID
+        valid = battery.do_charge_action(self.charge_rate)
+        return valid
+
+    def summarize_state(self, n_steps=None) -> dict:
+        if n_steps == h.STEPS_START:
+            summary = super().summarize_state(n_steps=n_steps)
+            return summary
+        else:
+            {}

environments/factory/additional/btry/btry_util.py

@@ -0,0 +1,30 @@
+from typing import NamedTuple, Union
+
+from environments.helpers import Constants as BaseConstants, EnvActions as BaseActions
+
+
+class Constants(BaseConstants):
+    # Battery Env
+    CHARGE_PODS          = 'Charge_Pod'
+    BATTERIES            = 'BATTERIES'
+    BATTERY_DISCHARGED   = 'DISCHARGED'
+    CHARGE_POD           = 1
+
+
+class Actions(BaseActions):
+    CHARGE              = 'do_charge_action'
+
+
+class RewardsBtry(NamedTuple):
+    CHARGE_VALID: float        = 0.1
+    CHARGE_FAIL: float         = -0.1
+    BATTERY_DISCHARGED: float  = -1.0
+
+
+class BatteryProperties(NamedTuple):
+    initial_charge: float = 0.8             #
+    charge_rate: float = 0.4                #
+    charge_locations: int = 20               #
+    per_action_costs: Union[dict, float] = 0.02
+    done_when_discharged: bool = False
+    multi_charge: bool = False

environments/factory/additional/btry/factory_battery.py

@@ -0,0 +1,139 @@
+from typing import Dict, List
+
+import numpy as np
+
+from environments.factory.additional.btry.btry_collections import BatteriesRegister, ChargePods
+from environments.factory.additional.btry.btry_util import Constants, Actions, RewardsBtry, BatteryProperties
+from environments.factory.base.base_factory import BaseFactory
+from environments.factory.base.objects import Agent, Action
+from environments.factory.base.renderer import RenderEntity
+
+c = Constants
+a = Actions
+
+
+class BatteryFactory(BaseFactory):
+
+    def __init__(self, *args, btry_prop=BatteryProperties(), rewards_btry: RewardsBtry = RewardsBtry(),
+                 **kwargs):
+        if isinstance(btry_prop, dict):
+            btry_prop = BatteryProperties(**btry_prop)
+        if isinstance(rewards_btry, dict):
+            rewards_btry = RewardsBtry(**rewards_btry)
+        self.btry_prop = btry_prop
+        self.rewards_dest = rewards_btry
+        super().__init__(*args, **kwargs)
+
+    def per_agent_raw_observations_hook(self, agent) -> Dict[str, np.typing.ArrayLike]:
+        additional_raw_observations = super().per_agent_raw_observations_hook(agent)
+        additional_raw_observations.update({c.BATTERIES: self[c.BATTERIES].as_array_by_entity(agent)})
+        return additional_raw_observations
+
+    def observations_hook(self) -> Dict[str, np.typing.ArrayLike]:
+        additional_observations = super().observations_hook()
+        additional_observations.update({c.CHARGE_PODS: self[c.CHARGE_PODS].as_array()})
+        return additional_observations
+
+    @property
+    def entities_hook(self):
+        super_entities = super().entities_hook
+
+        empty_tiles = self[c.FLOOR].empty_tiles[:self.btry_prop.charge_locations]
+        charge_pods = ChargePods.from_tiles(
+            empty_tiles, self._level_shape,
+            entity_kwargs=dict(charge_rate=self.btry_prop.charge_rate,
+                               multi_charge=self.btry_prop.multi_charge)
+        )
+
+        batteries = BatteriesRegister(self._level_shape if not self._pomdp_r else ((self.pomdp_diameter,) * 2),
+                                      )
+        batteries.spawn_batteries(self[c.AGENT], self.btry_prop.initial_charge)
+        super_entities.update({c.BATTERIES: batteries, c.CHARGE_PODS: charge_pods})
+        return super_entities
+
+    def step_hook(self) -> (List[dict], dict):
+        super_reward_info = super(BatteryFactory, self).step_hook()
+
+        # Decharge
+        batteries = self[c.BATTERIES]
+
+        for agent in self[c.AGENT]:
+            if isinstance(self.btry_prop.per_action_costs, dict):
+                energy_consumption = self.btry_prop.per_action_costs[agent.temp_action]
+            else:
+                energy_consumption = self.btry_prop.per_action_costs
+
+            batteries.by_entity(agent).decharge(energy_consumption)
+
+        return super_reward_info
+
+    def do_charge_action(self, agent) -> (dict, dict):
+        if charge_pod := self[c.CHARGE_PODS].by_pos(agent.pos):
+            valid = charge_pod.charge_battery(self[c.BATTERIES].by_entity(agent))
+            if valid:
+                info_dict = {f'{agent.name}_{a.CHARGE}_VALID': 1}
+                self.print(f'{agent.name} just charged batteries at {charge_pod.name}.')
+            else:
+                info_dict = {f'{agent.name}_{a.CHARGE}_FAIL': 1}
+                self.print(f'{agent.name} failed to charged batteries at {charge_pod.name}.')
+        else:
+            valid = c.NOT_VALID
+            info_dict = {f'{agent.name}_{a.CHARGE}_FAIL': 1}
+            # info_dict = {f'{agent.name}_no_charger': 1}
+            self.print(f'{agent.name} failed to charged batteries at {agent.pos}.')
+        reward = dict(value=self.rewards_dest.CHARGE_VALID if valid else self.rewards_dest.CHARGE_FAIL,
+                      reason=a.CHARGE, info=info_dict)
+        return valid, reward
+
+    def do_additional_actions(self, agent: Agent, action: Action) -> (bool, dict):
+        action_result = super().do_additional_actions(agent, action)
+        if action_result is None:
+            if action == a.CHARGE:
+                action_result = self.do_charge_action(agent)
+                return action_result
+            else:
+                return None
+        else:
+            return action_result
+        pass
+
+    def reset_hook(self) -> (List[dict], dict):
+        super_reward_info = super(BatteryFactory, self).reset_hook()
+        # There is Nothing to reset.
+        return super_reward_info
+
+    def check_additional_done(self) -> (bool, dict):
+        super_done, super_dict = super(BatteryFactory, self).check_additional_done()
+        if super_done:
+            return super_done, super_dict
+        else:
+            if self.btry_prop.done_when_discharged:
+                if btry_done := any(battery.is_discharged for battery in self[c.BATTERIES]):
+                    super_dict.update(DISCHARGE_DONE=1)
+                    return btry_done, super_dict
+                else:
+                    pass
+            else:
+                pass
+        return super_done, super_dict
+
+    def per_agent_reward_hook(self, agent: Agent) -> List[dict]:
+        reward_event_list = super(BatteryFactory, self).per_agent_reward_hook(agent)
+        if self[c.BATTERIES].by_entity(agent).is_discharged:
+            self.print(f'{agent.name} Battery is discharged!')
+            info_dict = {f'{agent.name}_{c.BATTERY_DISCHARGED}': 1}
+            reward_event_list.append({'value': self.rewards_dest.BATTERY_DISCHARGED,
+                                      'reason': c.BATTERY_DISCHARGED,
+                                      'info': info_dict}
+                                     )
+        else:
+            # All Fine
+            pass
+        return reward_event_list
+
+    def render_assets_hook(self):
+        # noinspection PyUnresolvedReferences
+        additional_assets = super().render_assets_hook()
+        charge_pods = [RenderEntity(c.CHARGE_PODS, charge_pod.tile.pos) for charge_pod in self[c.CHARGE_PODS]]
+        additional_assets.extend(charge_pods)
+        return additional_assets

environments/factory/additional/combined_factories.py

@@ -1,12 +1,15 @@
 import random
 
-from environments.factory.factory_battery import BatteryFactory, BatteryProperties
-from environments.factory.factory_dest import DestFactory
-from environments.factory.factory_dirt import DirtFactory, DirtProperties
-from environments.factory.factory_item import ItemFactory
-
 
 # noinspection PyAbstractClass
+from environments.factory.additional.btry.btry_util import BatteryProperties
+from environments.factory.additional.btry.factory_battery import BatteryFactory
+from environments.factory.additional.dest.factory_dest import DestFactory
+from environments.factory.additional.dirt.dirt_util import DirtProperties
+from environments.factory.additional.dirt.factory_dirt import DirtFactory
+from environments.factory.additional.item.factory_item import ItemFactory
+
+
 class DirtItemFactory(ItemFactory, DirtFactory):
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
@@ -24,6 +27,12 @@ class DirtDestItemFactory(ItemFactory, DirtFactory, DestFactory):
         super().__init__(*args, **kwargs)
 
 
+# noinspection PyAbstractClass
+class DestBatteryFactory(BatteryFactory, DestFactory):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+
 if __name__ == '__main__':
     from environments.utility_classes import AgentRenderOptions as ARO, ObservationProperties
 

environments/factory/additional/dest/dest_collections.py

@@ -0,0 +1,44 @@
+from environments.factory.base.registers import EntityCollection
+from environments.factory.additional.dest.dest_util import Constants as c
+from environments.factory.additional.dest.dest_enitites import Destination
+
+
+class Destinations(EntityCollection):
+
+    _accepted_objects = Destination
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.is_blocking_light = False
+        self.can_be_shadowed = False
+
+    def as_array(self):
+        self._array[:] = c.FREE_CELL
+        # ToDo: Switch to new Style Array Put
+        # indices = list(zip(range(len(cls)), *zip(*[x.pos for x in cls])))
+        # np.put(cls._array, [np.ravel_multi_index(x, cls._array.shape) for x in indices], cls.encodings)
+        for item in self:
+            if item.pos != c.NO_POS:
+                self._array[0, item.x, item.y] = item.encoding
+        return self._array
+
+    def __repr__(self):
+        return super(Destinations, self).__repr__()
+
+    def summarize_states(self, n_steps=None):
+        return {}
+
+
+class ReachedDestinations(Destinations):
+    _accepted_objects = Destination
+
+    def __init__(self, *args, **kwargs):
+        super(ReachedDestinations, self).__init__(*args, **kwargs)
+        self.can_be_shadowed = False
+        self.is_blocking_light = False
+
+    def summarize_states(self, n_steps=None):
+        return {}
+
+    def __repr__(self):
+        return super(ReachedDestinations, self).__repr__()

environments/factory/additional/dest/dest_enitites.py

@@ -0,0 +1,44 @@
+from collections import defaultdict
+
+from environments.factory.base.objects import Entity, Agent
+from environments.factory.additional.dest.dest_util import Constants as c
+
+
+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 encoding(self):
+        return c.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 do_wait_action(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.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

environments/factory/additional/dest/dest_util.py

@@ -0,0 +1,41 @@
+from typing import NamedTuple
+
+from environments.helpers import Constants as BaseConstants, EnvActions as BaseActions
+
+
+class Constants(BaseConstants):
+    # Destination Env
+    DEST                    = 'Destination'
+    DESTINATION             = 1
+    DESTINATION_DONE        = 0.5
+    DEST_REACHED            = 'ReachedDestination'
+
+
+class Actions(BaseActions):
+    WAIT_ON_DEST    = 'WAIT'
+
+
+class RewardsDest(NamedTuple):
+
+    WAIT_VALID: float      = 0.1
+    WAIT_FAIL: float       = -0.1
+    DEST_REACHED: float    = 5.0
+
+
+class DestModeOptions(object):
+    DONE        = 'DONE'
+    GROUPED     = 'GROUPED'
+    PER_DEST    = 'PER_DEST'
+
+
+class DestProperties(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 = DestModeOptions.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 == DestModeOptions.DONE) != bool(spawn_frequency)

environments/factory/additional/dest/factory_dest.py

@@ -1,132 +1,19 @@
 import time
-from collections import defaultdict
 from enum import Enum
-from typing import List, Union, NamedTuple, Dict
+from typing import List, Union, Dict
 import numpy as np
 import random
 
+from environments.factory.additional.dest.dest_collections import Destinations, ReachedDestinations
+from environments.factory.additional.dest.dest_enitites import Destination
+from environments.factory.additional.dest.dest_util import Constants, Actions, RewardsDest, DestModeOptions, \
+    DestProperties
 from environments.factory.base.base_factory import BaseFactory
-from environments.helpers import Constants as BaseConstants
+from environments.factory.base.objects import Agent, Action
-from environments.helpers import EnvActions as BaseActions
+from environments.factory.base.registers import Entities
-from environments.factory.base.objects import Agent, Entity, Action
-from environments.factory.base.registers import Entities, EntityCollection
 
 from environments.factory.base.renderer import RenderEntity
 
-
-class Constants(BaseConstants):
-    # Destination Env
-    DEST                    = 'Destination'
-    DESTINATION             = 1
-    DESTINATION_DONE        = 0.5
-    DEST_REACHED            = 'ReachedDestination'
-
-
-class Actions(BaseActions):
-    WAIT_ON_DEST    = 'WAIT'
-
-
-class RewardsDest(NamedTuple):
-
-    WAIT_VALID: float      = 0.1
-    WAIT_FAIL: float       = -0.1
-    DEST_REACHED: float    = 5.0
-
-
-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 encoding(self):
-        return c.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 do_wait_action(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.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(EntityCollection):
-
-    _accepted_objects = Destination
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        self.is_blocking_light = False
-        self.can_be_shadowed = False
-
-    def as_array(self):
-        self._array[:] = c.FREE_CELL
-        # ToDo: Switch to new Style Array Put
-        # indices = list(zip(range(len(cls)), *zip(*[x.pos for x in cls])))
-        # np.put(cls._array, [np.ravel_multi_index(x, cls._array.shape) for x in indices], cls.encodings)
-        for item in self:
-            if item.pos != c.NO_POS:
-                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
-
-    def __init__(self, *args, **kwargs):
-        super(ReachedDestinations, self).__init__(*args, **kwargs)
-        self.can_be_shadowed = False
-        self.is_blocking_light = False
-
-    def summarize_states(self, n_steps=None):
-        return {}
-
-
-class DestModeOptions(object):
-    DONE        = 'DONE'
-    GROUPED     = 'GROUPED'
-    PER_DEST    = 'PER_DEST'
-
-
-class DestProperties(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 = DestModeOptions.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 == DestModeOptions.DONE) != bool(spawn_frequency)
-
-
 c = Constants
 a = Actions
 
@@ -151,6 +38,7 @@ class DestFactory(BaseFactory):
     def actions_hook(self) -> Union[Action, List[Action]]:
         # noinspection PyUnresolvedReferences
         super_actions = super().actions_hook
+        # If targets are considers reached after some time, agents need an action for that.
         if self.dest_prop.dwell_time:
             super_actions.append(Action(enum_ident=a.WAIT_ON_DEST))
         return super_actions
@@ -207,7 +95,7 @@ class DestFactory(BaseFactory):
         if destinations_to_spawn:
             n_dest_to_spawn = len(destinations_to_spawn)
             if self.dest_prop.spawn_mode != DestModeOptions.GROUPED:
-                destinations = [Destination(tile, c.DEST) for tile in self[c.FLOOR].empty_tiles[:n_dest_to_spawn]]
+                destinations = [Destination(tile, self[c.DEST]) for tile in self[c.FLOOR].empty_tiles[:n_dest_to_spawn]]
                 self[c.DEST].add_additional_items(destinations)
                 for dest in destinations_to_spawn:
                     del self._dest_spawn_timer[dest]
@@ -229,9 +117,10 @@ class DestFactory(BaseFactory):
         super_reward_info = super().step_hook()
         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.DEST].values()):
             if dest.is_considered_reached:
-                dest.change_parent_collection(self[c.DEST])
+                dest.change_parent_collection(self[c.DEST_REACHED])
                 self._dest_spawn_timer[dest.name] = 0
                 self.print(f'{dest.name} is reached now, removing...')
             else:
@@ -251,18 +140,19 @@ class DestFactory(BaseFactory):
         additional_observations.update({c.DEST: self[c.DEST].as_array()})
         return additional_observations
 
-    def per_agent_reward_hook(self, agent: Agent) -> Dict[str, dict]:
+    def per_agent_reward_hook(self, agent: Agent) -> List[dict]:
         # noinspection PyUnresolvedReferences
-        reward_event_dict = super().per_agent_reward_hook(agent)
+        reward_event_list = super().per_agent_reward_hook(agent)
         if len(self[c.DEST_REACHED]):
             for reached_dest in list(self[c.DEST_REACHED]):
                 if agent.pos == reached_dest.pos:
                     self.print(f'{agent.name} just reached destination at {agent.pos}')
                     self[c.DEST_REACHED].delete_env_object(reached_dest)
                     info_dict = {f'{agent.name}_{c.DEST_REACHED}': 1}
-                    reward_event_dict.update({c.DEST_REACHED: {'reward': self.rewards_dest.DEST_REACHED,
+                    reward_event_list.append({'value': self.rewards_dest.DEST_REACHED,
-                                                               'info': info_dict}})
+                                              'reason': c.DEST_REACHED,
-        return reward_event_dict
+                                              'info': info_dict})
+        return reward_event_list
 
     def render_assets_hook(self, mode='human'):
         # noinspection PyUnresolvedReferences

environments/factory/additional/dirt/dirt_collections.py

@@ -0,0 +1,42 @@
+from environments.factory.additional.dirt.dirt_entity import Dirt
+from environments.factory.additional.dirt.dirt_util import DirtProperties
+from environments.factory.base.objects import Floor
+from environments.factory.base.registers import EntityCollection
+from environments.factory.additional.dirt.dirt_util import Constants as c
+
+
+class DirtRegister(EntityCollection):
+
+    _accepted_objects = Dirt
+
+    @property
+    def amount(self):
+        return sum([dirt.amount for dirt in self])
+
+    @property
+    def dirt_properties(self):
+        return self._dirt_properties
+
+    def __init__(self, dirt_properties, *args):
+        super(DirtRegister, self).__init__(*args)
+        self._dirt_properties: DirtProperties = dirt_properties
+
+    def spawn_dirt(self, then_dirty_tiles) -> bool:
+        if isinstance(then_dirty_tiles, Floor):
+            then_dirty_tiles = [then_dirty_tiles]
+        for tile in then_dirty_tiles:
+            if not self.amount > self.dirt_properties.max_global_amount:
+                dirt = self.by_pos(tile.pos)
+                if dirt is None:
+                    dirt = Dirt(tile, self, amount=self.dirt_properties.max_spawn_amount)
+                    self.add_item(dirt)
+                else:
+                    new_value = dirt.amount + self.dirt_properties.max_spawn_amount
+                    dirt.set_new_amount(min(new_value, self.dirt_properties.max_local_amount))
+            else:
+                return c.NOT_VALID
+        return c.VALID
+
+    def __repr__(self):
+        s = super(DirtRegister, self).__repr__()
+        return f'{s[:-1]}, {self.amount})'

environments/factory/additional/dirt/dirt_entity.py

@@ -0,0 +1,26 @@
+from environments.factory.base.objects import Entity
+
+
+class Dirt(Entity):
+
+    @property
+    def amount(self):
+        return self._amount
+
+    @property
+    def encoding(self):
+        # Edit this if you want items to be drawn in the ops differntly
+        return self._amount
+
+    def __init__(self, *args, amount=None, **kwargs):
+        super(Dirt, self).__init__(*args, **kwargs)
+        self._amount = amount
+
+    def set_new_amount(self, amount):
+        self._amount = amount
+        self._collection.notify_change_to_value(self)
+
+    def summarize_state(self, **kwargs):
+        state_dict = super().summarize_state(**kwargs)
+        state_dict.update(amount=float(self.amount))
+        return state_dict

environments/factory/additional/dirt/dirt_util.py

@@ -0,0 +1,30 @@
+from typing import NamedTuple
+
+from environments.helpers import Constants as BaseConstants, EnvActions as BaseActions
+
+
+class Constants(BaseConstants):
+    DIRT = 'Dirt'
+
+
+class Actions(BaseActions):
+    CLEAN_UP = 'do_cleanup_action'
+
+
+class RewardsDirt(NamedTuple):
+    CLEAN_UP_VALID: float          = 0.5
+    CLEAN_UP_FAIL: float           = -0.1
+    CLEAN_UP_LAST_PIECE: float     = 4.5
+
+
+class DirtProperties(NamedTuple):
+    initial_dirt_ratio: float = 0.3         # On INIT, on max how many tiles does the dirt spawn in percent.
+    initial_dirt_spawn_r_var: float = 0.05  # How much does the dirt spawn amount vary?
+    clean_amount: float = 1                 # How much does the robot clean with one actions.
+    max_spawn_ratio: float = 0.20           # On max how many tiles does the dirt spawn in percent.
+    max_spawn_amount: float = 0.3           # How much dirt does spawn per tile at max.
+    spawn_frequency: int = 0                # Spawn Frequency in Steps.
+    max_local_amount: int = 2               # Max dirt amount per tile.
+    max_global_amount: int = 20             # Max dirt amount in the whole environment.
+    dirt_smear_amount: float = 0.2          # Agents smear dirt, when not cleaning up in place.
+    done_when_clean: bool = True

environments/factory/additional/dirt/factory_dirt.py

@@ -1,111 +1,22 @@
 import time
 from pathlib import Path
-from typing import List, Union, NamedTuple, Dict
+from typing import List, Union, Dict
 import random
 
 import numpy as np
 
-from algorithms.TSP_dirt_agent import TSPDirtAgent
+from environments.factory.additional.dirt.dirt_collections import DirtRegister
-from environments.helpers import Constants as BaseConstants
+from environments.factory.additional.dirt.dirt_entity import Dirt
-from environments.helpers import EnvActions as BaseActions
+from environments.factory.additional.dirt.dirt_util import Constants, Actions, RewardsDirt, DirtProperties
 
 from environments.factory.base.base_factory import BaseFactory
-from environments.factory.base.objects import Agent, Action, Entity, Floor
+from environments.factory.base.objects import Agent, Action
-from environments.factory.base.registers import Entities, EntityCollection
+from environments.factory.base.registers import Entities
 
 from environments.factory.base.renderer import RenderEntity
 from environments.utility_classes import ObservationProperties
 
 
-class Constants(BaseConstants):
-    DIRT = 'Dirt'
-
-
-class Actions(BaseActions):
-    CLEAN_UP = 'do_cleanup_action'
-
-
-class RewardsDirt(NamedTuple):
-    CLEAN_UP_VALID: float          = 0.5
-    CLEAN_UP_FAIL: float           = -0.1
-    CLEAN_UP_LAST_PIECE: float     = 4.5
-
-
-class DirtProperties(NamedTuple):
-    initial_dirt_ratio: float = 0.3         # On INIT, on max how many tiles does the dirt spawn in percent.
-    initial_dirt_spawn_r_var: float = 0.05  # How much does the dirt spawn amount vary?
-    clean_amount: float = 1                 # How much does the robot clean with one actions.
-    max_spawn_ratio: float = 0.20           # On max how many tiles does the dirt spawn in percent.
-    max_spawn_amount: float = 0.3           # How much dirt does spawn per tile at max.
-    spawn_frequency: int = 0                # Spawn Frequency in Steps.
-    max_local_amount: int = 2               # Max dirt amount per tile.
-    max_global_amount: int = 20             # Max dirt amount in the whole environment.
-    dirt_smear_amount: float = 0.2          # Agents smear dirt, when not cleaning up in place.
-    done_when_clean: bool = True
-
-
-class Dirt(Entity):
-
-    @property
-    def amount(self):
-        return self._amount
-
-    @property
-    def encoding(self):
-        # Edit this if you want items to be drawn in the ops differntly
-        return self._amount
-
-    def __init__(self, *args, amount=None, **kwargs):
-        super(Dirt, self).__init__(*args, **kwargs)
-        self._amount = amount
-
-    def set_new_amount(self, amount):
-        self._amount = amount
-        self._collection.notify_change_to_value(self)
-
-    def summarize_state(self, **kwargs):
-        state_dict = super().summarize_state(**kwargs)
-        state_dict.update(amount=float(self.amount))
-        return state_dict
-
-
-class DirtRegister(EntityCollection):
-
-    _accepted_objects = Dirt
-
-    @property
-    def amount(self):
-        return sum([dirt.amount for dirt in self])
-
-    @property
-    def dirt_properties(self):
-        return self._dirt_properties
-
-    def __init__(self, dirt_properties, *args):
-        super(DirtRegister, self).__init__(*args)
-        self._dirt_properties: DirtProperties = dirt_properties
-
-    def spawn_dirt(self, then_dirty_tiles) -> bool:
-        if isinstance(then_dirty_tiles, Floor):
-            then_dirty_tiles = [then_dirty_tiles]
-        for tile in then_dirty_tiles:
-            if not self.amount > self.dirt_properties.max_global_amount:
-                dirt = self.by_pos(tile.pos)
-                if dirt is None:
-                    dirt = Dirt(tile, self, amount=self.dirt_properties.max_spawn_amount)
-                    self.add_item(dirt)
-                else:
-                    new_value = dirt.amount + self.dirt_properties.max_spawn_amount
-                    dirt.set_new_amount(min(new_value, self.dirt_properties.max_local_amount))
-            else:
-                return c.NOT_VALID
-        return c.VALID
-
-    def __repr__(self):
-        s = super(DirtRegister, self).__repr__()
-        return f'{s[:-1]}, {self.amount})'
-
-
 def softmax(x):
     """Compute softmax values for each sets of scores in x."""
     e_x = np.exp(x - np.max(x))
@@ -200,8 +111,8 @@ class DirtFactory(BaseFactory):
         super_reward_info = super().step_hook()
         if smear_amount := self.dirt_prop.dirt_smear_amount:
             for agent in self[c.AGENT]:
-                if agent.temp_valid and agent.last_pos != c.NO_POS:
+                if agent.step_result['action_valid'] and agent.last_pos != c.NO_POS:
-                    if self._actions.is_moving_action(agent.temp_action):
+                    if self._actions.is_moving_action(agent.step_result['action_name']):
                         if old_pos_dirt := self[c.DIRT].by_pos(agent.last_pos):
                             if smeared_dirt := round(old_pos_dirt.amount * smear_amount, 2):
                                 old_pos_dirt.set_new_amount(max(0, old_pos_dirt.amount-smeared_dirt))
@@ -248,8 +159,8 @@ class DirtFactory(BaseFactory):
         additional_observations.update({c.DIRT: self[c.DIRT].as_array()})
         return additional_observations
 
-    def gather_additional_info(self, agent: Agent) -> dict:
+    def post_step_hook(self) -> List[Dict[str, int]]:
-        event_reward_dict = super().per_agent_reward_hook(agent)
+        super_post_step = super(DirtFactory, self).post_step_hook()
         info_dict = dict()
 
         dirt = [dirt.amount for dirt in self[c.DIRT]]
@@ -264,8 +175,8 @@ class DirtFactory(BaseFactory):
         info_dict.update(dirt_amount=current_dirt_amount)
         info_dict.update(dirty_tile_count=dirty_tile_count)
 
-        event_reward_dict.update({'info': info_dict})
+        super_post_step.append(info_dict)
-        return event_reward_dict
+        return super_post_step
 
 
 if __name__ == '__main__':
@@ -304,7 +215,6 @@ if __name__ == '__main__':
                               # inject_agents=[TSPDirtAgent],
                               )
 
-        factory.save_params(Path('rewards_param'))
 
         # noinspection DuplicatedCode
         n_actions = factory.action_space.n - 1

environments/factory/additional/item/factory_item.py

@@ -1,179 +1,16 @@
 import time
-from collections import deque
+from typing import List, Union, Dict
-from typing import List, Union, NamedTuple, Dict
 import numpy as np
 import random
 
+from environments.factory.additional.item.item_collections import ItemRegister, Inventories, DropOffLocations
+from environments.factory.additional.item.item_util import Constants, Actions, RewardsItem, ItemProperties
 from environments.factory.base.base_factory import BaseFactory
-from environments.helpers import Constants as BaseConstants
+from environments.factory.base.objects import Agent, Action
-from environments.helpers import EnvActions as BaseActions
+from environments.factory.base.registers import Entities
-from environments import helpers as h
-from environments.factory.base.objects import Agent, Entity, Action, Floor
-from environments.factory.base.registers import Entities, EntityCollection, BoundEnvObjCollection, ObjectCollection
 
 from environments.factory.base.renderer import RenderEntity
 
-
-class Constants(BaseConstants):
-    NO_ITEM = 0
-    ITEM_DROP_OFF = 1
-    # Item Env
-    ITEM                = 'Item'
-    INVENTORY           = 'Inventory'
-    DROP_OFF            = 'Drop_Off'
-
-
-class Actions(BaseActions):
-    ITEM_ACTION     = 'ITEMACTION'
-
-
-class RewardsItem(NamedTuple):
-    DROP_OFF_VALID: float = 0.1
-    DROP_OFF_FAIL: float = -0.1
-    PICK_UP_FAIL: float  = -0.1
-    PICK_UP_VALID: float  = 0.1
-
-
-class Item(Entity):
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        self._auto_despawn = -1
-
-    @property
-    def auto_despawn(self):
-        return self._auto_despawn
-
-    @property
-    def encoding(self):
-        # Edit this if you want items to be drawn in the ops differently
-        return 1
-
-    def set_auto_despawn(self, auto_despawn):
-        self._auto_despawn = auto_despawn
-
-    def set_tile_to(self, no_pos_tile):
-        assert self._collection.__class__.__name__ != ItemRegister.__class__
-        self._tile = no_pos_tile
-
-
-class ItemRegister(EntityCollection):
-
-    _accepted_objects = Item
-
-    def spawn_items(self, tiles: List[Floor]):
-        items = [Item(tile, self) for tile in tiles]
-        self.add_additional_items(items)
-
-    def despawn_items(self, items: List[Item]):
-        items = [items] if isinstance(items, Item) else items
-        for item in items:
-            del self[item]
-
-
-class Inventory(BoundEnvObjCollection):
-
-    @property
-    def name(self):
-        return f'{self.__class__.__name__}({self._bound_entity.name})'
-
-    def __init__(self, agent: Agent, capacity: int, *args, **kwargs):
-        super(Inventory, self).__init__(agent, *args,  is_blocking_light=False, can_be_shadowed=False,  **kwargs)
-        self.capacity = capacity
-
-    def as_array(self):
-        if self._array is None:
-            self._array = np.zeros((1, *self._shape))
-        return super(Inventory, self).as_array()
-
-    def summarize_states(self, **kwargs):
-        attr_dict = {key: str(val) for key, val in self.__dict__.items() if not key.startswith('_') and key != 'data'}
-        attr_dict.update(dict(items={key: val.summarize_state(**kwargs) for key, val in self.items()}))
-        attr_dict.update(dict(name=self.name))
-        return attr_dict
-
-    def pop(self):
-        item_to_pop = self[0]
-        self.delete_env_object(item_to_pop)
-        return item_to_pop
-
-
-class Inventories(ObjectCollection):
-
-    _accepted_objects = Inventory
-    is_blocking_light = False
-    can_be_shadowed = False
-
-    def __init__(self, obs_shape, *args, **kwargs):
-        super(Inventories, self).__init__(*args, is_per_agent=True, individual_slices=True, **kwargs)
-        self._obs_shape = obs_shape
-
-    def as_array(self):
-        return np.stack([inventory.as_array() for inv_idx, inventory in enumerate(self)])
-
-    def spawn_inventories(self, agents, capacity):
-        inventories = [self._accepted_objects(agent, capacity, self._obs_shape)
-                       for _, agent in enumerate(agents)]
-        self.add_additional_items(inventories)
-
-    def idx_by_entity(self, entity):
-        try:
-            return next((idx for idx, inv in enumerate(self) if inv.belongs_to_entity(entity)))
-        except StopIteration:
-            return None
-
-    def by_entity(self, entity):
-        try:
-            return next((inv for inv in self if inv.belongs_to_entity(entity)))
-        except StopIteration:
-            return None
-
-    def summarize_states(self, **kwargs):
-        return {key: val.summarize_states(**kwargs) for key, val in self.items()}
-
-
-class DropOffLocation(Entity):
-
-    @property
-    def encoding(self):
-        return Constants.ITEM_DROP_OFF
-
-    def __init__(self, *args, storage_size_until_full: int = 5, auto_item_despawn_interval: int = 5, **kwargs):
-        super(DropOffLocation, self).__init__(*args, **kwargs)
-        self.auto_item_despawn_interval = auto_item_despawn_interval
-        self.storage = deque(maxlen=storage_size_until_full or None)
-
-    def place_item(self, item: Item):
-        if self.is_full:
-            raise RuntimeWarning("There is currently no way to clear the storage or make it unfull.")
-            return c.NOT_VALID
-        else:
-            self.storage.append(item)
-            item.set_auto_despawn(self.auto_item_despawn_interval)
-            return c.VALID
-
-    @property
-    def is_full(self):
-        return False if not self.storage.maxlen else self.storage.maxlen == len(self.storage)
-
-    def summarize_state(self, n_steps=None) -> dict:
-        if n_steps == h.STEPS_START:
-            return super().summarize_state(n_steps=n_steps)
-
-
-class DropOffLocations(EntityCollection):
-
-    _accepted_objects = DropOffLocation
-
-
-class ItemProperties(NamedTuple):
-    n_items:                         int  = 5     # How many items are there at the same time
-    spawn_frequency:                 int  = 10     # Spawn Frequency in Steps
-    n_drop_off_locations:            int  = 5     # How many DropOff locations are there at the same time
-    max_dropoff_storage_size:        int  = 0     # How many items are needed until the dropoff is full
-    max_agent_inventory_capacity:    int  = 5     # How many items are needed until the agent inventory is full
-
-
 c = Constants
 a = Actions
 

environments/factory/additional/item/item_collections.py

@@ -0,0 +1,87 @@
+from typing import List
+
+import numpy as np
+
+from environments.factory.base.objects import Floor, Agent
+from environments.factory.base.registers import EntityCollection, BoundEnvObjCollection, ObjectCollection
+from environments.factory.additional.item.item_entities import Item, DropOffLocation
+
+
+class ItemRegister(EntityCollection):
+
+    _accepted_objects = Item
+
+    def spawn_items(self, tiles: List[Floor]):
+        items = [Item(tile, self) for tile in tiles]
+        self.add_additional_items(items)
+
+    def despawn_items(self, items: List[Item]):
+        items = [items] if isinstance(items, Item) else items
+        for item in items:
+            del self[item]
+
+
+class Inventory(BoundEnvObjCollection):
+
+    @property
+    def name(self):
+        return f'{self.__class__.__name__}({self._bound_entity.name})'
+
+    def __init__(self, agent: Agent, capacity: int, *args, **kwargs):
+        super(Inventory, self).__init__(agent, *args,  is_blocking_light=False, can_be_shadowed=False,  **kwargs)
+        self.capacity = capacity
+
+    def as_array(self):
+        if self._array is None:
+            self._array = np.zeros((1, *self._shape))
+        return super(Inventory, self).as_array()
+
+    def summarize_states(self, **kwargs):
+        attr_dict = {key: str(val) for key, val in self.__dict__.items() if not key.startswith('_') and key != 'data'}
+        attr_dict.update(dict(items={key: val.summarize_state(**kwargs) for key, val in self.items()}))
+        attr_dict.update(dict(name=self.name))
+        return attr_dict
+
+    def pop(self):
+        item_to_pop = self[0]
+        self.delete_env_object(item_to_pop)
+        return item_to_pop
+
+
+class Inventories(ObjectCollection):
+
+    _accepted_objects = Inventory
+    is_blocking_light = False
+    can_be_shadowed = False
+
+    def __init__(self, obs_shape, *args, **kwargs):
+        super(Inventories, self).__init__(*args, is_per_agent=True, individual_slices=True, **kwargs)
+        self._obs_shape = obs_shape
+
+    def as_array(self):
+        return np.stack([inventory.as_array() for inv_idx, inventory in enumerate(self)])
+
+    def spawn_inventories(self, agents, capacity):
+        inventories = [self._accepted_objects(agent, capacity, self._obs_shape)
+                       for _, agent in enumerate(agents)]
+        self.add_additional_items(inventories)
+
+    def idx_by_entity(self, entity):
+        try:
+            return next((idx for idx, inv in enumerate(self) if inv.belongs_to_entity(entity)))
+        except StopIteration:
+            return None
+
+    def by_entity(self, entity):
+        try:
+            return next((inv for inv in self if inv.belongs_to_entity(entity)))
+        except StopIteration:
+            return None
+
+    def summarize_states(self, **kwargs):
+        return {key: val.summarize_states(**kwargs) for key, val in self.items()}
+
+
+class DropOffLocations(EntityCollection):
+
+    _accepted_objects = DropOffLocation

environments/factory/additional/item/item_entities.py

@@ -0,0 +1,61 @@
+from collections import deque
+
+from environments import helpers as h
+from environments.factory.additional.item.item_util import Constants
+from environments.factory.base.objects import Entity
+
+
+class Item(Entity):
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self._auto_despawn = -1
+
+    @property
+    def auto_despawn(self):
+        return self._auto_despawn
+
+    @property
+    def encoding(self):
+        # Edit this if you want items to be drawn in the ops differently
+        return 1
+
+    def set_auto_despawn(self, auto_despawn):
+        self._auto_despawn = auto_despawn
+
+    def set_tile_to(self, no_pos_tile):
+        self._tile = no_pos_tile
+
+    def summarize_state(self, **_) -> dict:
+        super_summarization = super(Item, self).summarize_state()
+        super_summarization.update(dict(auto_despawn=self.auto_despawn))
+        return super_summarization
+
+
+class DropOffLocation(Entity):
+
+    @property
+    def encoding(self):
+        return Constants.ITEM_DROP_OFF
+
+    def __init__(self, *args, storage_size_until_full: int = 5, auto_item_despawn_interval: int = 5, **kwargs):
+        super(DropOffLocation, self).__init__(*args, **kwargs)
+        self.auto_item_despawn_interval = auto_item_despawn_interval
+        self.storage = deque(maxlen=storage_size_until_full or None)
+
+    def place_item(self, item: Item):
+        if self.is_full:
+            raise RuntimeWarning("There is currently no way to clear the storage or make it unfull.")
+            return c.NOT_VALID
+        else:
+            self.storage.append(item)
+            item.set_auto_despawn(self.auto_item_despawn_interval)
+            return Constants.VALID
+
+    @property
+    def is_full(self):
+        return False if not self.storage.maxlen else self.storage.maxlen == len(self.storage)
+
+    def summarize_state(self, n_steps=None) -> dict:
+        if n_steps == h.STEPS_START:
+            return super().summarize_state(n_steps=n_steps)

environments/factory/additional/item/item_util.py

@@ -0,0 +1,31 @@
+from typing import NamedTuple
+
+from environments.helpers import Constants as BaseConstants, EnvActions as BaseActions
+
+
+class Constants(BaseConstants):
+    NO_ITEM = 0
+    ITEM_DROP_OFF = 1
+    # Item Env
+    ITEM                = 'Item'
+    INVENTORY           = 'Inventory'
+    DROP_OFF            = 'Drop_Off'
+
+
+class Actions(BaseActions):
+    ITEM_ACTION     = 'ITEMACTION'
+
+
+class RewardsItem(NamedTuple):
+    DROP_OFF_VALID: float = 0.1
+    DROP_OFF_FAIL: float = -0.1
+    PICK_UP_FAIL: float  = -0.1
+    PICK_UP_VALID: float  = 0.1
+
+
+class ItemProperties(NamedTuple):
+    n_items:                         int  = 5     # How many items are there at the same time
+    spawn_frequency:                 int  = 10     # Spawn Frequency in Steps
+    n_drop_off_locations:            int  = 5     # How many DropOff locations are there at the same time
+    max_dropoff_storage_size:        int  = 0     # How many items are needed until the dropoff is full
+    max_agent_inventory_capacity:    int  = 5     # How many items are needed until the agent inventory is full

environments/factory/base/base_factory.py

@@ -180,6 +180,7 @@ class BaseFactory(gym.Env):
                 self._entities.add_additional_items({c.DOORS: doors})
 
         # Actions
+        # TODO: Move this to Agent init, so that agents can have individual action sets.
         self._actions = Actions(self.mv_prop, can_use_doors=self.parse_doors)
         if additional_actions := self.actions_hook:
             self._actions.add_additional_items(additional_actions)
@@ -308,7 +309,8 @@ class BaseFactory(gym.Env):
             info.update(self._summarize_state())
 
         # Post step Hook for later use
-        info.update(self.post_step_hook())
+        for post_step_info in self.post_step_hook():
+            info.update(post_step_info)
 
         obs, _ = self._build_observations()
 
@@ -367,14 +369,16 @@ class BaseFactory(gym.Env):
                         agent_obs = global_agent_obs.copy()
                         agent_obs[(0, *agent.pos)] -= agent.encoding
                 else:
-                    agent_obs = global_agent_obs
+                    agent_obs = global_agent_obs.copy()
             else:
+                # agent_obs == None!!!!!
                 agent_obs = global_agent_obs
 
             # Build Level Observations
             if self.obs_prop.render_agents == a_obs.LEVEL:
+                assert agent_obs is not None
                 lvl_obs = lvl_obs.copy()
-                lvl_obs += global_agent_obs
+                lvl_obs += agent_obs
 
             obs_dict[c.WALLS] = lvl_obs
             if self.obs_prop.render_agents in [a_obs.SEPERATE, a_obs.COMBINED] and agent_obs is not None:
@@ -600,7 +604,9 @@ class BaseFactory(gym.Env):
             for reward in agent.step_result['rewards']:
                 combined_info_dict.update(reward['info'])
 
+        # Combine Info dicts into a global one
         combined_info_dict = dict(combined_info_dict)
+
         combined_info_dict.update(info)
 
         global_reward_sum = sum(global_env_rewards)
@@ -616,9 +622,11 @@ class BaseFactory(gym.Env):
 
     def start_recording(self):
         self._record_episodes = True
+        return self._record_episodes
 
     def stop_recording(self):
         self._record_episodes = False
+        return not self._record_episodes
 
     # noinspection PyGlobalUndefined
     def render(self, mode='human'):
@@ -719,12 +727,12 @@ class BaseFactory(gym.Env):
         return {}
 
     @abc.abstractmethod
-    def per_agent_reward_hook(self, agent: Agent) -> Dict[str, dict]:
+    def per_agent_reward_hook(self, agent: Agent) -> List[dict]:
-        return {}
+        return []
 
     @abc.abstractmethod
-    def post_step_hook(self) -> dict:
+    def post_step_hook(self) -> List[dict]:
-        return {}
+        return []
 
     @abc.abstractmethod
     def per_agent_raw_observations_hook(self, agent) -> Dict[str, np.typing.ArrayLike]:

environments/factory/base/objects.py

@@ -119,7 +119,6 @@ class Entity(EnvObject):
 
     def __repr__(self):
         return super(Entity, self).__repr__() + f'(@{self.pos})'
-# With Position in Env
 
 
 # TODO: Missing Documentation

environments/factory/base/registers.py

@@ -117,7 +117,7 @@ class EnvObjectCollection(ObjectCollection):
         return self._array
 
     def summarize_states(self, n_steps=None):
-        return [val.summarize_state(n_steps=n_steps) for val in self.values()]
+        return [entity.summarize_state(n_steps=n_steps) for entity in self.values()]
 
     def notify_change_to_free(self, env_object: EnvObject):
         self._array_change_notifyer(env_object, value=c.FREE_CELL)

environments/factory/factory_battery.py

@@ -1,273 +0,0 @@
-from typing import Union, NamedTuple, Dict, List
-
-import numpy as np
-
-from environments.factory.base.base_factory import BaseFactory
-from environments.factory.base.objects import Agent, Action, Entity, EnvObject, BoundingMixin
-from environments.factory.base.registers import EntityCollection, EnvObjectCollection
-from environments.factory.base.renderer import RenderEntity
-from environments.helpers import Constants as BaseConstants
-from environments.helpers import EnvActions as BaseActions
-
-from environments import helpers as h
-
-
-class Constants(BaseConstants):
-    # Battery Env
-    CHARGE_PODS          = 'Charge_Pod'
-    BATTERIES            = 'BATTERIES'
-    BATTERY_DISCHARGED   = 'DISCHARGED'
-    CHARGE_POD           = 1
-
-
-class Actions(BaseActions):
-    CHARGE              = 'do_charge_action'
-
-
-class RewardsBtry(NamedTuple):
-    CHARGE_VALID: float        = 0.1
-    CHARGE_FAIL: float         = -0.1
-    BATTERY_DISCHARGED: float  = -1.0
-
-
-class BatteryProperties(NamedTuple):
-    initial_charge: float = 0.8             #
-    charge_rate: float = 0.4                #
-    charge_locations: int = 20               #
-    per_action_costs: Union[dict, float] = 0.02
-    done_when_discharged = False
-    multi_charge: bool = False
-
-
-c = Constants
-a = Actions
-
-
-class Battery(BoundingMixin, EnvObject):
-
-    @property
-    def is_discharged(self):
-        return self.charge_level == 0
-
-    def __init__(self, initial_charge_level: float, *args, **kwargs):
-        super(Battery, self).__init__(*args, **kwargs)
-        self.charge_level = initial_charge_level
-
-    def encoding(self):
-        return self.charge_level
-
-    def do_charge_action(self, amount):
-        if self.charge_level < 1:
-            # noinspection PyTypeChecker
-            self.charge_level = min(1, amount + self.charge_level)
-            return c.VALID
-        else:
-            return c.NOT_VALID
-
-    def decharge(self, amount) -> c:
-        if self.charge_level != 0:
-            # noinspection PyTypeChecker
-            self.charge_level = max(0, amount + self.charge_level)
-            self._collection.notify_change_to_value(self)
-            return c.VALID
-        else:
-            return c.NOT_VALID
-
-    def summarize_state(self, **_):
-        attr_dict = {key: str(val) for key, val in self.__dict__.items() if not key.startswith('_') and key != 'data'}
-        attr_dict.update(dict(name=self.name))
-        return attr_dict
-
-
-class BatteriesRegister(EnvObjectCollection):
-
-    _accepted_objects = Battery
-
-    def __init__(self, *args, **kwargs):
-        super(BatteriesRegister, self).__init__(*args, individual_slices=True,
-                                                is_blocking_light=False, can_be_shadowed=False, **kwargs)
-        self.is_observable = True
-
-    def spawn_batteries(self, agents, initial_charge_level):
-        batteries = [self._accepted_objects(initial_charge_level, agent, self) for _, agent in enumerate(agents)]
-        self.add_additional_items(batteries)
-
-    def summarize_states(self, n_steps=None):
-        # as dict with additional nesting
-        # return dict(items=super(Inventories, cls).summarize_states())
-        return super(BatteriesRegister, self).summarize_states(n_steps=n_steps)
-
-    # Todo Move this to Mixin!
-    def by_entity(self, entity):
-        try:
-            return next((x for x in self if x.belongs_to_entity(entity)))
-        except StopIteration:
-            return None
-
-    def idx_by_entity(self, entity):
-        try:
-            return next((idx for idx, x in enumerate(self) if x.belongs_to_entity(entity)))
-        except StopIteration:
-            return None
-
-    def as_array_by_entity(self, entity):
-        return self._array[self.idx_by_entity(entity)]
-
-
-class ChargePod(Entity):
-
-    @property
-    def encoding(self):
-        return c.CHARGE_POD
-
-    def __init__(self, *args, charge_rate: float = 0.4,
-                 multi_charge: bool = False, **kwargs):
-        super(ChargePod, self).__init__(*args, **kwargs)
-        self.charge_rate = charge_rate
-        self.multi_charge = multi_charge
-
-    def charge_battery(self, battery: Battery):
-        if battery.charge_level == 1.0:
-            return c.NOT_VALID
-        if sum(guest for guest in self.tile.guests if 'agent' in guest.name.lower()) > 1:
-            return c.NOT_VALID
-        valid = battery.do_charge_action(self.charge_rate)
-        return valid
-
-    def summarize_state(self, n_steps=None) -> dict:
-        if n_steps == h.STEPS_START:
-            summary = super().summarize_state(n_steps=n_steps)
-            return summary
-
-
-class ChargePods(EntityCollection):
-
-    _accepted_objects = ChargePod
-
-    def __repr__(self):
-        super(ChargePods, self).__repr__()
-
-
-class BatteryFactory(BaseFactory):
-
-    def __init__(self, *args, btry_prop=BatteryProperties(), rewards_dest: RewardsBtry = RewardsBtry(),
-                 **kwargs):
-        if isinstance(btry_prop, dict):
-            btry_prop = BatteryProperties(**btry_prop)
-        if isinstance(rewards_dest, dict):
-            rewards_dest = BatteryProperties(**rewards_dest)
-        self.btry_prop = btry_prop
-        self.rewards_dest = rewards_dest
-        super().__init__(*args, **kwargs)
-
-    def per_agent_raw_observations_hook(self, agent) -> Dict[str, np.typing.ArrayLike]:
-        additional_raw_observations = super().per_agent_raw_observations_hook(agent)
-        additional_raw_observations.update({c.BATTERIES: self[c.BATTERIES].as_array_by_entity(agent)})
-        return additional_raw_observations
-
-    def observations_hook(self) -> Dict[str, np.typing.ArrayLike]:
-        additional_observations = super().observations_hook()
-        additional_observations.update({c.CHARGE_PODS: self[c.CHARGE_PODS].as_array()})
-        return additional_observations
-
-    @property
-    def entities_hook(self):
-        super_entities = super().entities_hook
-
-        empty_tiles = self[c.FLOOR].empty_tiles[:self.btry_prop.charge_locations]
-        charge_pods = ChargePods.from_tiles(
-            empty_tiles, self._level_shape,
-            entity_kwargs=dict(charge_rate=self.btry_prop.charge_rate,
-                               multi_charge=self.btry_prop.multi_charge)
-        )
-
-        batteries = BatteriesRegister(self._level_shape if not self._pomdp_r else ((self.pomdp_diameter,) * 2),
-                                      )
-        batteries.spawn_batteries(self[c.AGENT], self.btry_prop.initial_charge)
-        super_entities.update({c.BATTERIES: batteries, c.CHARGE_PODS: charge_pods})
-        return super_entities
-
-    def step_hook(self) -> (List[dict], dict):
-        super_reward_info = super(BatteryFactory, self).step_hook()
-
-        # Decharge
-        batteries = self[c.BATTERIES]
-
-        for agent in self[c.AGENT]:
-            if isinstance(self.btry_prop.per_action_costs, dict):
-                energy_consumption = self.btry_prop.per_action_costs[agent.temp_action]
-            else:
-                energy_consumption = self.btry_prop.per_action_costs
-
-            batteries.by_entity(agent).decharge(energy_consumption)
-
-        return super_reward_info
-
-    def do_charge_action(self, agent) -> (dict, dict):
-        if charge_pod := self[c.CHARGE_PODS].by_pos(agent.pos):
-            valid = charge_pod.charge_battery(self[c.BATTERIES].by_entity(agent))
-            if valid:
-                info_dict = {f'{agent.name}_{a.CHARGE}_VALID': 1}
-                self.print(f'{agent.name} just charged batteries at {charge_pod.name}.')
-            else:
-                info_dict = {f'{agent.name}_{a.CHARGE}_FAIL': 1}
-                self.print(f'{agent.name} failed to charged batteries at {charge_pod.name}.')
-        else:
-            valid = c.NOT_VALID
-            info_dict = {f'{agent.name}_{a.CHARGE}_FAIL': 1}
-            # info_dict = {f'{agent.name}_no_charger': 1}
-            self.print(f'{agent.name} failed to charged batteries at {agent.pos}.')
-        reward = dict(value=self.rewards_dest.CHARGE_VALID if valid else self.rewards_dest.CHARGE_FAIL,
-                      reason=a.CHARGE, info=info_dict)
-        return valid, reward
-
-    def do_additional_actions(self, agent: Agent, action: Action) -> (bool, dict):
-        action_result = super().do_additional_actions(agent, action)
-        if action_result is None:
-            if action == a.CHARGE:
-                action_result = self.do_charge_action(agent)
-                return action_result
-            else:
-                return None
-        else:
-            return action_result
-        pass
-
-    def reset_hook(self) -> None:
-        # There is Nothing to reset.
-        pass
-
-    def check_additional_done(self) -> (bool, dict):
-        super_done, super_dict = super(BatteryFactory, self).check_additional_done()
-        if super_done:
-            return super_done, super_dict
-        else:
-            if self.btry_prop.done_when_discharged:
-                if btry_done := any(battery.is_discharged for battery in self[c.BATTERIES]):
-                    super_dict.update(DISCHARGE_DONE=1)
-                    return btry_done, super_dict
-                else:
-                    pass
-            else:
-                pass
-        pass
-
-    def per_agent_reward_hook(self, agent: Agent) -> Dict[str, dict]:
-        reward_event_dict = super(BatteryFactory, self).per_agent_reward_hook(agent)
-        if self[c.BATTERIES].by_entity(agent).is_discharged:
-            self.print(f'{agent.name} Battery is discharged!')
-            info_dict = {f'{agent.name}_{c.BATTERY_DISCHARGED}': 1}
-            reward_event_dict.update({c.BATTERY_DISCHARGED: {'reward': self.rewards_dest.BATTERY_DISCHARGED,
-                                                             'info': info_dict}}
-                                     )
-        else:
-            # All Fine
-            pass
-        return reward_event_dict
-
-    def render_assets_hook(self):
-        # noinspection PyUnresolvedReferences
-        additional_assets = super().render_assets_hook()
-        charge_pods = [RenderEntity(c.CHARGE_PODS, charge_pod.tile.pos) for charge_pod in self[c.CHARGE_PODS]]
-        additional_assets.extend(charge_pods)
-        return additional_assets

environments/factory/factory_dirt_stationary_machines.py

@@ -3,7 +3,9 @@ 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.factory_dirt import DirtFactory
+from environments.factory.additional.dirt.dirt_collections import DirtRegister
+from environments.factory.additional.dirt.dirt_entity import Dirt
 from environments.factory.base.objects import Floor
 from environments.factory.base.registers import Floors, Entities, EntityCollection
 
@@ -28,7 +30,6 @@ class StationaryMachinesDirtFactory(DirtFactory):
 
     def entities_hook(self) -> Dict[(str, Entities)]:
         super_entities = super().entities_hook()
-
         return super_entities
 
     def reset_hook(self) -> None:
@@ -48,8 +49,8 @@ class StationaryMachinesDirtFactory(DirtFactory):
         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]:
+    def per_agent_reward_hook(self, agent: Agent) -> List[dict]:
-        pass
+        return super(StationaryMachinesDirtFactory, self).per_agent_reward_hook(agent)
 
     def pre_step_hook(self) -> None:
         pass

environments/helpers.py

@@ -173,9 +173,9 @@ class RewardsBase(NamedTuple):
 
 class ObservationTranslator:
 
-    def __init__(self, obs_shape_2d: (int, int), this_named_observation_space: Dict[str, dict],
+    def __init__(self, this_named_observation_space: Dict[str, dict],
                  *per_agent_named_obs_spaces: Dict[str, dict],
-                 placeholder_fill_value: Union[int, str] = 'N'):
+                 placeholder_fill_value: Union[int, str, None] = None):
         """
         This is a helper class, which converts agents observations from joined environments.
         For example, agents trained in different environments may expect different observations.
@@ -183,8 +183,6 @@ class ObservationTranslator:
         A string identifier based approach is used.
         Currently, it is not possible to mix different obs shapes.
 
-        :param obs_shape_2d: The shape of the observation the agents expect.
-        :type  obs_shape_2d: tuple(int, int)
 
         :param this_named_observation_space: `Named observation space` of the joined environment.
         :type  this_named_observation_space: Dict[str, dict]
@@ -196,15 +194,15 @@ class ObservationTranslator:
         :type  placeholder_fill_value: Union[int, str] = 'N')
         """
 
-        assert len(obs_shape_2d) == 2
-        self.obs_shape = obs_shape_2d
         if isinstance(placeholder_fill_value, str):
             if placeholder_fill_value.lower() in ['normal', 'n']:
-                self.random_fill = lambda: np.random.normal(size=self.obs_shape)
+                self.random_fill = np.random.normal
             elif placeholder_fill_value.lower() in ['uniform', 'u']:
-                self.random_fill = lambda: np.random.uniform(size=self.obs_shape)
+                self.random_fill = np.random.uniform
             else:
-                raise ValueError('Please chooe between "uniform" or "normal"')
+                raise ValueError('Please chooe between "uniform" or "normal" ("u", "n").')
+        elif isinstance(placeholder_fill_value, int):
+            raise NotImplementedError('"Future Work."')
         else:
             self.random_fill = None
 
@@ -213,9 +211,21 @@ class ObservationTranslator:
 
     def translate_observation(self, agent_idx: int, obs: np.ndarray):
         target_obs_space = self._per_agent_named_obs_space[agent_idx]
-        translation = [idx_space_dict for name, idx_space_dict in target_obs_space.items()]
+        translation = dict()
-        flat_translation = [x for y in translation for x in y]
+        for name, idxs in target_obs_space.items():
-        return np.take(obs, flat_translation, axis=1 if obs.ndim == 4 else 0)
+            if name in self._this_named_obs_space:
+                for target_idx, this_idx in zip(idxs, self._this_named_obs_space[name]):
+                    taken_slice = np.take(obs, [this_idx], axis=1 if obs.ndim == 4 else 0)
+                    translation[target_idx] = taken_slice
+            elif random_fill := self.random_fill:
+                for target_idx in idxs:
+                    translation[target_idx] = random_fill(size=obs.shape[:-3] + (1,) + obs.shape[-2:])
+            else:
+                for target_idx in idxs:
+                    translation[target_idx] = np.zeros(shape=(obs.shape[:-3] + (1,) + obs.shape[-2:]))
+
+        translation = dict(sorted(translation.items()))
+        return np.concatenate(list(translation.values()), axis=-3)
 
     def translate_observations(self, observations: List[ArrayLike]):
         return [self.translate_observation(idx, observation) for idx, observation in enumerate(observations)]
@@ -241,6 +251,9 @@ class ActionTranslator:
         """
 
         self._target_named_action_space = target_named_action_space
+        if isinstance(per_agent_named_action_space, (list, tuple)):
+            self._per_agent_named_action_space = per_agent_named_action_space
+        else:
             self._per_agent_named_action_space = list(per_agent_named_action_space)
         self._per_agent_idx_actions = [{idx: a for a, idx in x.items()} for x in self._per_agent_named_action_space]
 

environments/logging/envmonitor.py

@@ -71,8 +71,8 @@ class EnvMonitor(BaseCallback):
             pass
         return
 
-    def save_run(self, filepath: Union[Path, str], auto_plotting_keys=None):
+    def save_run(self, filepath: Union[Path, str, None] = None, auto_plotting_keys=None):
-        filepath = Path(filepath)
+        filepath = Path(filepath or self._filepath)
         filepath.parent.mkdir(exist_ok=True, parents=True)
         with filepath.open('wb') as f:
             pickle.dump(self._monitor_df.reset_index(), f, protocol=pickle.HIGHEST_PROTOCOL)

environments/logging/recorder.py

@@ -1,4 +1,5 @@
 from collections import defaultdict
+from os import PathLike
 from pathlib import Path
 from typing import Union
 
@@ -12,11 +13,14 @@ from environments.factory.base.base_factory import REC_TAC
 
 class EnvRecorder(BaseCallback):
 
-    def __init__(self, env, entities='all'):
+    def __init__(self, env, entities: str = 'all', filepath: Union[str, PathLike] = None, freq: int = 0):
         super(EnvRecorder, self).__init__()
+        self.filepath = filepath
         self.unwrapped = env
+        self.freq = freq
         self._recorder_dict = defaultdict(list)
         self._recorder_out_list = list()
+        self._episode_counter = 1
         if isinstance(entities, str):
             if entities.lower() == 'all':
                 self._entities = None
@@ -29,46 +33,47 @@ class EnvRecorder(BaseCallback):
         return getattr(self.unwrapped, item)
 
     def reset(self):
-        self.unwrapped.start_recording()
+        self._on_training_start()
         return self.unwrapped.reset()
 
     def _on_training_start(self) -> None:
-        self.unwrapped._record_episodes = True
+        assert self.start_recording()
-        pass
 
     def _read_info(self, env_idx, info: dict):
         if info_dict := {key.replace(REC_TAC, ''): val for key, val in info.items() if key.startswith(f'{REC_TAC}')}:
             if self._entities:
                 info_dict = {k: v for k, v in info_dict.items() if k in self._entities}
-
-            info_dict.update(episode=(self.num_timesteps + env_idx))
             self._recorder_dict[env_idx].append(info_dict)
         else:
             pass
-        return
+        return True
 
     def _read_done(self, env_idx, done):
         if done:
             self._recorder_out_list.append({'steps': self._recorder_dict[env_idx],
-                                            'episode': len(self._recorder_out_list)})
+                                            'episode': self._episode_counter})
             self._recorder_dict[env_idx] = list()
         else:
             pass
 
     def step(self, actions):
         step_result = self.unwrapped.step(actions)
-        # 0, 1,     2    ,     3    =    idx
+        self._on_step()
-        # _, _, done_bool, info_obj = step_result
-        self._read_info(0, step_result[3])
-        self._read_done(0, step_result[2])
         return step_result
 
-    def save_records(self, filepath: Union[Path, str], save_occupation_map=False, save_trajectory_map=False):
+    def finalize(self):
-        filepath = Path(filepath)
+        self._on_training_end()
+        return True
+
+    def save_records(self, filepath: Union[Path, str, None] = None, save_occupation_map=False, save_trajectory_map=False):
+        filepath = Path(filepath or self.filepath)
         filepath.parent.mkdir(exist_ok=True, parents=True)
         # cls.out_file.unlink(missing_ok=True)
         with filepath.open('w') as f:
-            out_dict = {'episodes': self._recorder_out_list, 'header': self.unwrapped.params}
+            out_dict = {'n_episodes': self._episode_counter,
+                        'header': self.unwrapped.params,
+                        'episodes': self._recorder_out_list
+                        }
             try:
                 simplejson.dump(out_dict, f, indent=4)
             except TypeError:
@@ -76,6 +81,7 @@ class EnvRecorder(BaseCallback):
 
         if save_occupation_map:
             a = np.zeros((15, 15))
+            # noinspection PyTypeChecker
             for episode in out_dict['episodes']:
                 df = pd.DataFrame([y for x in episode['steps'] for y in x['Agents']])
 
@@ -94,15 +100,22 @@ class EnvRecorder(BaseCallback):
             raise NotImplementedError('This has not yet been implemented.')
 
     def _on_step(self) -> bool:
+        do_record = self.freq == -1 or self._episode_counter % self.freq == 0
         for env_idx, info in enumerate(self.locals.get('infos', [])):
+            if do_record:
                 self._read_info(env_idx, info)
-
         dones = list(enumerate(self.locals.get('dones', [])))
         dones.extend(list(enumerate(self.locals.get('done', []))))
         for env_idx, done in dones:
+            if do_record:
                 self._read_done(env_idx, done)
-
+            if done:
+                self._episode_counter += 1
         return True
 
     def _on_training_end(self) -> None:
+        for env_idx in range(len(self._recorder_dict)):
+            if self._recorder_dict[env_idx]:
+                self._recorder_out_list.append({'steps': self._recorder_dict[env_idx],
+                                                'episode': self._episode_counter})
         pass

environments/utility_classes.py

@@ -3,6 +3,21 @@ import gym
 from gym.wrappers.frame_stack import FrameStack
 
 
+class EnvCombiner(object):
+
+    def __init__(self, *envs_cls):
+        self._env_dict = {env_cls.__name__: env_cls for env_cls in envs_cls}
+
+    @staticmethod
+    def combine_cls(name, *envs_cls):
+        return type(name,envs_cls,{})
+
+    def build(self):
+        name = f'{"".join([x.lower().replace("factory").capitalize() for x in self._env_dict.keys()])}Factory'
+
+        return self.combine_cls(name, tuple(self._env_dict.values()))
+
+
 class AgentRenderOptions(object):
     """
     Class that specifies the available options for the way agents are represented in the env observation.
@@ -46,7 +61,7 @@ class ObservationProperties(NamedTuple):
     Property holder; for setting multiple related parameters through a single parameter. Comes with default values.
     """
 
-    """How to represent agents in the observation space. This may also alters the obs-shape."""
+    """How to represent agents in the observation space. This may also alter the obs-shape."""
     render_agents: AgentRenderOptions = AgentRenderOptions.SEPERATE
 
     """Obserations are build per agent; whether the current agent should be represented in its own observation."""

plotting/compare_runs.py

@@ -76,10 +76,11 @@ def compare_seed_runs(run_path: Union[str, PathLike], use_tex: bool = False):
         skip_n = round(df_melted['Episode'].max() * 0.02)
         df_melted = df_melted[df_melted['Episode'] % skip_n == 0]
 
-    if run_path.is_dir():
+    run_path.mkdir(parents=True, exist_ok=True)
-        prepare_plot(run_path / f'{run_path}_monitor_lineplot.png', df_melted, use_tex=use_tex)
+    if run_path.exists() and run_path.is_file():
-    elif run_path.exists() and run_path.is_file():
+        prepare_plot(run_path.parent / f'{run_path.name}_monitor_lineplot.png', df_melted, use_tex=use_tex)
-        prepare_plot(run_path.parent / f'{run_path.parent}_monitor_lineplot.png', df_melted, use_tex=use_tex)
+    else:
+        prepare_plot(run_path / f'{run_path.name}_monitor_lineplot.png', df_melted, use_tex=use_tex)
     print('Plotting done.')
 
 

quickstart/combine_and_monitor_rerun.py

@@ -0,0 +1,187 @@
+import sys
+from pathlib import Path
+
+##############################################
+# keep this for stand alone script execution #
+##############################################
+from environments.factory.base.base_factory import BaseFactory
+from environments.logging.recorder import EnvRecorder
+
+try:
+    # noinspection PyUnboundLocalVariable
+    if __package__ is None:
+        DIR = Path(__file__).resolve().parent
+        sys.path.insert(0, str(DIR.parent))
+        __package__ = DIR.name
+    else:
+        DIR = None
+except NameError:
+    DIR = None
+    pass
+##############################################
+##############################################
+##############################################
+
+
+import simplejson
+
+from environments import helpers as h
+from environments.factory.additional.combined_factories import DestBatteryFactory
+from environments.factory.additional.dest.factory_dest import DestFactory
+from environments.factory.additional.dirt.factory_dirt import DirtFactory
+from environments.factory.additional.item.factory_item import ItemFactory
+from environments.helpers import ObservationTranslator, ActionTranslator
+from environments.logging.envmonitor import EnvMonitor
+from environments.utility_classes import ObservationProperties, AgentRenderOptions, MovementProperties
+
+
+def policy_model_kwargs():
+    return dict(ent_coef=0.01)
+
+
+def dqn_model_kwargs():
+    return dict(buffer_size=50000,
+                learning_starts=64,
+                batch_size=64,
+                target_update_interval=5000,
+                exploration_fraction=0.25,
+                exploration_final_eps=0.025
+                )
+
+
+def encapsule_env_factory(env_fctry, env_kwrgs):
+
+    def _init():
+        with env_fctry(**env_kwrgs) as init_env:
+            return init_env
+
+    return _init
+
+
+if __name__ == '__main__':
+
+    # Define Global Env Parameters
+    # Define properties object parameters
+    factory_kwargs = dict(
+        max_steps=400, parse_doors=True,
+        level_name='rooms',
+        doors_have_area=True, verbose=False,
+        mv_prop=MovementProperties(allow_diagonal_movement=True,
+                                   allow_square_movement=True,
+                                   allow_no_op=False),
+        obs_prop=ObservationProperties(
+            frames_to_stack=3,
+            cast_shadows=True,
+            omit_agent_self=True,
+            render_agents=AgentRenderOptions.LEVEL,
+            additional_agent_placeholder=None,
+        )
+    )
+
+    # Bundle both environments with global kwargs and parameters
+    # Todo: find a better solution, like outo module loading
+    env_map = {'DirtFactory': DirtFactory,
+               'ItemFactory': ItemFactory,
+               'DestFactory': DestFactory,
+               'DestBatteryFactory': DestBatteryFactory
+               }
+    env_names = list(env_map.keys())
+
+    # Put all your multi-seed agends in a single folder, we do not need specific names etc.
+    available_models = dict()
+    available_envs = dict()
+    available_runs_kwargs = dict()
+    available_runs_agents = dict()
+    max_seed = 0
+    # Define this folder
+    combinations_path = Path('combinations')
+    # Those are all differently trained combinations of mdoels, env and parameters
+    for combination in (x for x in combinations_path.iterdir() if x.is_dir()):
+        # These are all the models for this specific combination
+        for model_run in (x for x in combination.iterdir() if x.is_dir()):
+            model_name, env_name = model_run.name.split('_')[:2]
+            if model_name not in available_models:
+                available_models[model_name] = h.MODEL_MAP[model_name]
+            if env_name not in available_envs:
+                available_envs[env_name] = env_map[env_name]
+            # Those are all available seeds
+            for seed_run in (x for x in model_run.iterdir() if x.is_dir()):
+                max_seed = max(int(seed_run.name.split('_')[0]), max_seed)
+                # Read the env configuration from ROM
+                with next(seed_run.glob('env_params.json')).open('r') as f:
+                    env_kwargs = simplejson.load(f)
+                available_runs_kwargs[seed_run.name] = env_kwargs
+                # Read the trained model_path from ROM
+                model_path = next(seed_run.glob('model.zip'))
+                available_runs_agents[seed_run.name] = model_path
+
+    # We start by combining all SAME MODEL CLASSES per available Seed, across ALL available ENVIRONMENTS.
+    for model_name, model_cls in available_models.items():
+        for seed in range(max_seed):
+            combined_env_kwargs = dict()
+            model_paths = list()
+            comparable_runs = {key: val for key, val in available_runs_kwargs.items() if (
+                    key.startswith(str(seed)) and model_name in key and key != 'key')
+                               }
+            for name, run_kwargs in comparable_runs.items():
+                # Select trained agent as a candidate:
+                model_paths.append(available_runs_agents[name])
+                # Sort Env Kwars:
+                for key, val in run_kwargs.items():
+                    if key not in combined_env_kwargs:
+                        combined_env_kwargs.update(dict(key=val))
+                    else:
+                        assert combined_env_kwargs[key] == val, "Check the combinations you try to make!"
+
+            # Update and combine all kwargs to account for multiple agents etc.
+            # We cannot capture all configuration cases!
+            for key, val in factory_kwargs.items():
+                if key not in combined_env_kwargs:
+                    combined_env_kwargs[key] = val
+                else:
+                    assert combined_env_kwargs[key] == val
+            combined_env_kwargs.update(n_agents=len(comparable_runs))
+
+            with(type("CombinedEnv", tuple(available_envs.values()), {})(**combined_env_kwargs)) as combEnv:
+                # EnvMonitor Init
+                comb = f'comb_{model_name}_{seed}'
+                comb_monitor_path = combinations_path / comb / f'{comb}_monitor.pick'
+                comb_recorder_path = combinations_path / comb / f'{comb}_recorder.pick'
+                comb_monitor_path.parent.mkdir(parents=True, exist_ok=True)
+
+                monitoredCombEnv = EnvMonitor(combEnv, filepath=comb_monitor_path)
+                # monitoredCombEnv = EnvRecorder(monitoredCombEnv, filepath=comb_monitor_path)
+
+                # Evaluation starts here #####################################################
+                # Load all models
+                loaded_models = [available_models[model_name].load(model_path) for model_path in model_paths]
+                obs_translators = ObservationTranslator(
+                    monitoredCombEnv.named_observation_space,
+                    *[agent.named_observation_space for agent in loaded_models],
+                    placeholder_fill_value='n')
+                act_translators = ActionTranslator(
+                    monitoredCombEnv.named_action_space,
+                    *(agent.named_action_space for agent in loaded_models)
+                )
+
+                for episode in range(50):
+                    obs, _ = monitoredCombEnv.reset(), monitoredCombEnv.render()
+                    rew, done_bool = 0, False
+                    while not done_bool:
+                        actions = []
+                        for i, model in enumerate(loaded_models):
+                            pred = model.predict(obs_translators.translate_observation(i, obs[i]))[0]
+                            actions.append(act_translators.translate_action(i, pred))
+
+                        obs, step_r, done_bool, info_obj = monitoredCombEnv.step(actions)
+
+                        rew += step_r
+                        monitoredCombEnv.render()
+                        if done_bool:
+                            break
+                    print(f'Factory run {episode} done, reward is:\n    {rew}')
+                # Eval monitor outputs are automatically stored by the monitor object
+                # TODO: Plotting
+                monitoredCombEnv.save_records(comb_monitor_path)
+                monitoredCombEnv.save_run()
+            pass

quickstart/single_agent_train_battery_target_env.py

@@ -0,0 +1,203 @@
+import sys
+import time
+
+from pathlib import Path
+import simplejson
+
+import stable_baselines3 as sb3
+
+# This is needed, when you put this file in a subfolder.
+try:
+    # noinspection PyUnboundLocalVariable
+    if __package__ is None:
+        DIR = Path(__file__).resolve().parent
+        sys.path.insert(0, str(DIR.parent))
+        __package__ = DIR.name
+    else:
+        DIR = None
+except NameError:
+    DIR = None
+    pass
+
+from environments import helpers as h
+from environments.factory.additional.dest.dest_util import DestModeOptions, DestProperties
+from environments.factory.additional.btry.btry_util import BatteryProperties
+from environments.logging.envmonitor import EnvMonitor
+from environments.logging.recorder import EnvRecorder
+from environments.factory.additional.combined_factories import DestBatteryFactory
+from environments.utility_classes import MovementProperties, ObservationProperties, AgentRenderOptions
+
+from plotting.compare_runs import compare_seed_runs
+
+"""
+Welcome to this quick start file. Here we will see how to:
+    0. Setup I/O Paths
+    1. Setup parameters for the environments (dirt-factory).
+    2. Setup parameters for the agent training (SB3: PPO) and save metrics.
+        Run the training.
+    3. Save env and agent for later analysis.
+    4. Load the agent from drive
+    5. Rendering the env with a run of the trained agent.
+    6. Plot metrics 
+"""
+
+if __name__ == '__main__':
+    #########################################################
+    # 0. Setup I/O Paths
+    # Define some general parameters
+    train_steps = 1e6
+    n_seeds = 3
+    model_class = sb3.PPO
+    env_class = DestBatteryFactory
+
+    env_params_json = 'env_params.json'
+
+    # Define a global studi save path
+    start_time = int(time.time())
+    study_root_path = Path(__file__).parent.parent / 'study_out' / f'{Path(__file__).stem}_{start_time}'
+    # Create an identifier, which is unique for every combination and easy to read in filesystem
+    identifier = f'{model_class.__name__}_{env_class.__name__}_{start_time}'
+    exp_path = study_root_path / identifier
+
+    #########################################################
+    # 1. Setup parameters for the environments (dirt-factory).
+
+
+    # Define property object parameters.
+    #  'ObservationProperties' are for specifying how the agent sees the env.
+    obs_props = ObservationProperties(render_agents=AgentRenderOptions.NOT,  # Agents won`t be shown in the obs at all
+                                      omit_agent_self=True,                  # This is default
+                                      additional_agent_placeholder=None,     # We will not take care of future agents
+                                      frames_to_stack=3,                     # To give the agent a notion of time
+                                      pomdp_r=2                              # the agents view-radius
+                                      )
+    #  'MovementProperties' are for specifying how the agent is allowed to move in the env.
+    move_props = MovementProperties(allow_diagonal_movement=True,   # Euclidean style (vertices)
+                                    allow_square_movement=True,     # Manhattan (edges)
+                                    allow_no_op=False)              # Pause movement (do nothing)
+
+    #  'DirtProperties' control if and how dirt is spawned
+    # TODO: Comments
+    dest_props = DestProperties(
+        n_dests              = 2,  # How many destinations are there
+        dwell_time           = 0,  # How long does the agent need to "wait" on a destination
+        spawn_frequency      = 0,
+        spawn_in_other_zone  = True,  #
+        spawn_mode           = DestModeOptions.DONE,
+    )
+    btry_props = BatteryProperties(
+        initial_charge          = 0.9,  #
+        charge_rate             = 0.4,  #
+        charge_locations        = 3,  #
+        per_action_costs        = 0.01,
+        done_when_discharged    = True,
+        multi_charge            = False,
+    )
+
+    #  These are the EnvKwargs for initializing the env class, holding all former parameter-classes
+    # TODO: Comments
+    factory_kwargs = dict(n_agents=1,
+                          max_steps=400,
+                          parse_doors=True,
+                          level_name='rooms',
+                          doors_have_area=True,  #
+                          verbose=False,
+                          mv_prop=move_props,    # See Above
+                          obs_prop=obs_props,    # See Above
+                          done_at_collision=True,
+                          dest_prop=dest_props,
+                          btry_prop=btry_props
+                          )
+
+    #########################################################
+    # 2. Setup parameters for the agent training (SB3: PPO) and save metrics.
+    agent_kwargs = dict()
+
+
+    #########################################################
+    # Run the Training
+    for seed in range(n_seeds):
+        # Make a copy if you want to alter things in the training loop; like the seed.
+        env_kwargs = factory_kwargs.copy()
+        env_kwargs.update(env_seed=seed)
+
+        # Output folder
+        seed_path = exp_path / f'{str(seed)}_{identifier}'
+        seed_path.mkdir(parents=True, exist_ok=True)
+
+        # Parameter Storage
+        param_path = seed_path / env_params_json
+        # Observation (measures) Storage
+        monitor_path = seed_path / 'monitor.pick'
+        recorder_path = seed_path / 'recorder.json'
+        # Model save Path for the trained model
+        model_save_path = seed_path / f'model.zip'
+
+        # Env Init & Model kwargs definition
+        with env_class(**env_kwargs) as env_factory:
+
+            # EnvMonitor Init
+            env_monitor_callback = EnvMonitor(env_factory)
+
+            # EnvRecorder Init
+            env_recorder_callback = EnvRecorder(env_factory, freq=int(train_steps / 400 / 10))
+
+            # Model Init
+            model = model_class("MlpPolicy", env_factory, verbose=1, seed=seed, device='cpu')
+
+            # Model train
+            model.learn(total_timesteps=int(train_steps), callback=[env_monitor_callback, env_recorder_callback])
+
+            #########################################################
+            # 3. Save env and agent for later analysis.
+            #   Save the trained Model, the monitor (env measures) and the env parameters
+            model.named_observation_space = env_factory.named_observation_space
+            model.named_action_space = env_factory.named_action_space
+            model.save(model_save_path)
+            env_factory.save_params(param_path)
+            env_monitor_callback.save_run(monitor_path)
+            env_recorder_callback.save_records(recorder_path, save_occupation_map=False)
+
+    # Compare performance runs, for each seed within a model
+    try:
+        compare_seed_runs(exp_path, use_tex=False)
+    except ValueError:
+        pass
+
+    # Train ends here ############################################################
+
+    # Evaluation starts here #####################################################
+    # First Iterate over every model and monitor "as trained"
+    print('Start Measurement Tracking')
+    # For trained policy in study_root_path / identifier
+    for policy_path in [x for x in exp_path.iterdir() if x.is_dir()]:
+
+        # retrieve model class
+        model_cls = next(val for key, val in h.MODEL_MAP.items() if key in policy_path.parent.name)
+        # Load the agent agent
+        model = model_cls.load(policy_path / 'model.zip', device='cpu')
+        # Load old env kwargs
+        with next(policy_path.glob(env_params_json)).open('r') as f:
+            env_kwargs = simplejson.load(f)
+            # Make the env stop ar collisions
+            # (you only want to have a single collision per episode hence the statistics)
+            env_kwargs.update(done_at_collision=True)
+
+        # Init Env
+        with env_class(**env_kwargs) as env_factory:
+            monitored_env_factory = EnvMonitor(env_factory)
+
+            # Evaluation Loop for i in range(n Episodes)
+            for episode in range(100):
+                # noinspection PyRedeclaration
+                env_state = monitored_env_factory.reset()
+                rew, done_bool = 0, False
+                while not done_bool:
+                    action = model.predict(env_state, deterministic=True)[0]
+                    env_state, step_r, done_bool, info_obj = monitored_env_factory.step(action)
+                    rew += step_r
+                    if done_bool:
+                        break
+                print(f'Factory run {episode} done, reward is:\n    {rew}')
+            monitored_env_factory.save_run(filepath=policy_path / 'eval_run_monitor.pick')
+    print('Measurements Done')

quickstart/single_agent_train_dest_env.py

@@ -0,0 +1,193 @@
+import sys
+import time
+
+from pathlib import Path
+import simplejson
+
+import stable_baselines3 as sb3
+
+# This is needed, when you put this file in a subfolder.
+try:
+    # noinspection PyUnboundLocalVariable
+    if __package__ is None:
+        DIR = Path(__file__).resolve().parent
+        sys.path.insert(0, str(DIR.parent))
+        __package__ = DIR.name
+    else:
+        DIR = None
+except NameError:
+    DIR = None
+    pass
+
+from environments import helpers as h
+from environments.factory.additional.dest.dest_util import DestModeOptions, DestProperties
+from environments.logging.envmonitor import EnvMonitor
+from environments.logging.recorder import EnvRecorder
+from environments.factory.additional.dest.factory_dest import DestFactory
+from environments.utility_classes import MovementProperties, ObservationProperties, AgentRenderOptions
+
+from plotting.compare_runs import compare_seed_runs
+
+"""
+Welcome to this quick start file. Here we will see how to:
+    0. Setup I/O Paths
+    1. Setup parameters for the environments (dest-factory).
+    2. Setup parameters for the agent training (SB3: PPO) and save metrics.
+        Run the training.
+    3. Save env and agent for later analysis.
+    4. Load the agent from drive
+    5. Rendering the env with a run of the trained agent.
+    6. Plot metrics 
+"""
+
+if __name__ == '__main__':
+    #########################################################
+    # 0. Setup I/O Paths
+    # Define some general parameters
+    train_steps = 1e6
+    n_seeds = 3
+    model_class = sb3.PPO
+    env_class = DestFactory
+
+    env_params_json = 'env_params.json'
+
+    # Define a global studi save path
+    start_time = int(time.time())
+    study_root_path = Path(__file__).parent.parent / 'study_out' / f'{Path(__file__).stem}_{start_time}'
+    # Create an identifier, which is unique for every combination and easy to read in filesystem
+    identifier = f'{model_class.__name__}_{env_class.__name__}_{start_time}'
+    exp_path = study_root_path / identifier
+
+    #########################################################
+    # 1. Setup parameters for the environments (dest-factory).
+
+
+    # Define property object parameters.
+    #  'ObservationProperties' are for specifying how the agent sees the env.
+    obs_props = ObservationProperties(render_agents=AgentRenderOptions.NOT,  # Agents won`t be shown in the obs at all
+                                      omit_agent_self=True,                  # This is default
+                                      additional_agent_placeholder=None,     # We will not take care of future agents
+                                      frames_to_stack=3,                     # To give the agent a notion of time
+                                      pomdp_r=2                              # the agents view-radius
+                                      )
+    #  'MovementProperties' are for specifying how the agent is allowed to move in the env.
+    move_props = MovementProperties(allow_diagonal_movement=True,   # Euclidean style (vertices)
+                                    allow_square_movement=True,     # Manhattan (edges)
+                                    allow_no_op=False)              # Pause movement (do nothing)
+
+    #  'DestProperties' control if and how dest is spawned
+    # TODO: Comments
+    dest_props = DestProperties(
+        n_dests              = 2,  # How many destinations are there
+        dwell_time           = 0,  # How long does the agent need to "wait" on a destination
+        spawn_frequency      = 0,
+        spawn_in_other_zone  = True,  #
+        spawn_mode           = DestModeOptions.DONE,
+    )
+
+    #  These are the EnvKwargs for initializing the env class, holding all former parameter-classes
+    # TODO: Comments
+    factory_kwargs = dict(n_agents=1,
+                          max_steps=400,
+                          parse_doors=True,
+                          level_name='rooms',
+                          doors_have_area=True,  #
+                          verbose=False,
+                          mv_prop=move_props,    # See Above
+                          obs_prop=obs_props,    # See Above
+                          done_at_collision=True,
+                          dest_prop=dest_props
+                          )
+
+    #########################################################
+    # 2. Setup parameters for the agent training (SB3: PPO) and save metrics.
+    agent_kwargs = dict()
+
+
+    #########################################################
+    # Run the Training
+    for seed in range(n_seeds):
+        # Make a copy if you want to alter things in the training loop; like the seed.
+        env_kwargs = factory_kwargs.copy()
+        env_kwargs.update(env_seed=seed)
+
+        # Output folder
+        seed_path = exp_path / f'{str(seed)}_{identifier}'
+        seed_path.mkdir(parents=True, exist_ok=True)
+
+        # Parameter Storage
+        param_path = seed_path / env_params_json
+        # Observation (measures) Storage
+        monitor_path = seed_path / 'monitor.pick'
+        recorder_path = seed_path / 'recorder.json'
+        # Model save Path for the trained model
+        model_save_path = seed_path / f'model.zip'
+
+        # Env Init & Model kwargs definition
+        with env_class(**env_kwargs) as env_factory:
+
+            # EnvMonitor Init
+            env_monitor_callback = EnvMonitor(env_factory)
+
+            # EnvRecorder Init
+            env_recorder_callback = EnvRecorder(env_factory, freq=int(train_steps / 400 / 10))
+
+            # Model Init
+            model = model_class("MlpPolicy", env_factory,verbose=1, seed=seed, device='cpu')
+
+            # Model train
+            model.learn(total_timesteps=int(train_steps), callback=[env_monitor_callback, env_recorder_callback])
+
+            #########################################################
+            # 3. Save env and agent for later analysis.
+            #   Save the trained Model, the monitor (env measures) and the env parameters
+            model.named_observation_space = env_factory.named_observation_space
+            model.named_action_space = env_factory.named_action_space
+            model.save(model_save_path)
+            env_factory.save_params(param_path)
+            env_monitor_callback.save_run(monitor_path)
+            env_recorder_callback.save_records(recorder_path, save_occupation_map=False)
+
+    # Compare performance runs, for each seed within a model
+    try:
+        compare_seed_runs(exp_path, use_tex=False)
+    except ValueError:
+        pass
+
+    # Train ends here ############################################################
+
+    # Evaluation starts here #####################################################
+    # First Iterate over every model and monitor "as trained"
+    print('Start Measurement Tracking')
+    # For trained policy in study_root_path / identifier
+    for policy_path in [x for x in exp_path.iterdir() if x.is_dir()]:
+
+        # retrieve model class
+        model_cls = next(val for key, val in h.MODEL_MAP.items() if key in policy_path.parent.name)
+        # Load the agent agent
+        model = model_cls.load(policy_path / 'model.zip', device='cpu')
+        # Load old env kwargs
+        with next(policy_path.glob(env_params_json)).open('r') as f:
+            env_kwargs = simplejson.load(f)
+            # Make the env stop ar collisions
+            # (you only want to have a single collision per episode hence the statistics)
+            env_kwargs.update(done_at_collision=True)
+
+        # Init Env
+        with env_class(**env_kwargs) as env_factory:
+            monitored_env_factory = EnvMonitor(env_factory)
+
+            # Evaluation Loop for i in range(n Episodes)
+            for episode in range(100):
+                # noinspection PyRedeclaration
+                env_state = monitored_env_factory.reset()
+                rew, done_bool = 0, False
+                while not done_bool:
+                    action = model.predict(env_state, deterministic=True)[0]
+                    env_state, step_r, done_bool, info_obj = monitored_env_factory.step(action)
+                    rew += step_r
+                    if done_bool:
+                        break
+                print(f'Factory run {episode} done, reward is:\n    {rew}')
+            monitored_env_factory.save_run(filepath=policy_path / 'eval_run_monitor.pick')
+    print('Measurements Done')

quickstart/single_agent_train_dirt_env.py

@@ -1,11 +1,12 @@
 import sys
 import time
+
 from pathlib import Path
-from matplotlib import pyplot as plt
+import simplejson
-import itertools as it
 
 import stable_baselines3 as sb3
 
+# This is needed, when you put this file in a subfolder.
 try:
     # noinspection PyUnboundLocalVariable
     if __package__ is None:
@@ -18,19 +19,14 @@ except NameError:
     DIR = None
     pass
 
-import simplejson
-from stable_baselines3.common.vec_env import SubprocVecEnv
-
 from environments import helpers as h
-from environments.factory.factory_dirt import DirtProperties, DirtFactory
 from environments.logging.envmonitor import EnvMonitor
+from environments.logging.recorder import EnvRecorder
+from environments.factory.additional.dirt.dirt_util import DirtProperties
+from environments.factory.additional.dirt.factory_dirt import DirtFactory
 from environments.utility_classes import MovementProperties, ObservationProperties, AgentRenderOptions
-import pickle
-from plotting.compare_runs import compare_seed_runs, compare_model_runs
-import pandas as pd
-import seaborn as sns
 
-import multiprocessing as mp
+from plotting.compare_runs import compare_seed_runs
 
 """
 Welcome to this quick start file. Here we will see how to:
@@ -53,6 +49,8 @@ if __name__ == '__main__':
     model_class = sb3.PPO
     env_class = DirtFactory
 
+    env_params_json = 'env_params.json'
+
     # Define a global studi save path
     start_time = int(time.time())
     study_root_path = Path(__file__).parent.parent / 'study_out' / f'{Path(__file__).stem}_{start_time}'
@@ -100,7 +98,7 @@ if __name__ == '__main__':
                           mv_prop=move_props,    # See Above
                           obs_prop=obs_props,    # See Above
                           done_at_collision=True,
-                          dirt_props=dirt_props
+                          dirt_prop=dirt_props
                           )
 
     #########################################################
@@ -120,30 +118,37 @@ if __name__ == '__main__':
         seed_path.mkdir(parents=True, exist_ok=True)
 
         # Parameter Storage
-        param_path = seed_path / f'env_params.json'
+        param_path = seed_path / env_params_json
         # Observation (measures) Storage
         monitor_path = seed_path / 'monitor.pick'
+        recorder_path = seed_path / 'recorder.json'
         # Model save Path for the trained model
         model_save_path = seed_path / f'model.zip'
 
         # Env Init & Model kwargs definition
-        with DirtFactory(env_kwargs) as env_factory:
+        with env_class(**env_kwargs) as env_factory:
 
             # EnvMonitor Init
             env_monitor_callback = EnvMonitor(env_factory)
 
+            # EnvRecorder Init
+            env_recorder_callback = EnvRecorder(env_factory, freq=int(train_steps / 400 / 10))
+
             # Model Init
             model = model_class("MlpPolicy", env_factory,verbose=1, seed=seed, device='cpu')
 
             # Model train
-            model.learn(total_timesteps=int(train_steps), callback=[env_monitor_callback])
+            model.learn(total_timesteps=int(train_steps), callback=[env_monitor_callback, env_recorder_callback])
 
             #########################################################
             # 3. Save env and agent for later analysis.
             #   Save the trained Model, the monitor (env measures) and the env parameters
+            model.named_observation_space = env_factory.named_observation_space
+            model.named_action_space = env_factory.named_action_space
             model.save(model_save_path)
             env_factory.save_params(param_path)
             env_monitor_callback.save_run(monitor_path)
+            env_recorder_callback.save_records(recorder_path, save_occupation_map=False)
 
     # Compare performance runs, for each seed within a model
     try:
@@ -164,18 +169,19 @@ if __name__ == '__main__':
         # Load the agent agent
         model = model_cls.load(policy_path / 'model.zip', device='cpu')
         # Load old env kwargs
-        with next(policy_path.glob('*.json')).open('r') as f:
+        with next(policy_path.glob(env_params_json)).open('r') as f:
             env_kwargs = simplejson.load(f)
             # Make the env stop ar collisions
             # (you only want to have a single collision per episode hence the statistics)
             env_kwargs.update(done_at_collision=True)
 
         # Init Env
-        with env_to_run(**env_kwargs) as env_factory:
+        with env_class(**env_kwargs) as env_factory:
             monitored_env_factory = EnvMonitor(env_factory)
 
             # Evaluation Loop for i in range(n Episodes)
             for episode in range(100):
+                # noinspection PyRedeclaration
                 env_state = monitored_env_factory.reset()
                 rew, done_bool = 0, False
                 while not done_bool:
@@ -185,8 +191,5 @@ if __name__ == '__main__':
                     if done_bool:
                         break
                 print(f'Factory run {episode} done, reward is:\n    {rew}')
-            monitored_env_factory.save_run(filepath=policy_path / f'{baseline_monitor_file}.pick')
+            monitored_env_factory.save_run(filepath=policy_path / 'eval_run_monitor.pick')
-
-        # for policy_path in (y for y in policy_path.iterdir() if y.is_dir()):
-        #    load_model_run_baseline(policy_path)
     print('Measurements Done')

quickstart/single_agent_train_item_env.py

@@ -0,0 +1,191 @@
+import sys
+import time
+
+from pathlib import Path
+import simplejson
+
+import stable_baselines3 as sb3
+
+# This is needed, when you put this file in a subfolder.
+try:
+    # noinspection PyUnboundLocalVariable
+    if __package__ is None:
+        DIR = Path(__file__).resolve().parent
+        sys.path.insert(0, str(DIR.parent))
+        __package__ = DIR.name
+    else:
+        DIR = None
+except NameError:
+    DIR = None
+    pass
+
+from environments import helpers as h
+from environments.factory.additional.item.factory_item import ItemFactory
+from environments.factory.additional.item.item_util import ItemProperties
+from environments.logging.envmonitor import EnvMonitor
+from environments.logging.recorder import EnvRecorder
+from environments.utility_classes import MovementProperties, ObservationProperties, AgentRenderOptions
+
+from plotting.compare_runs import compare_seed_runs
+
+"""
+Welcome to this quick start file. Here we will see how to:
+    0. Setup I/O Paths
+    1. Setup parameters for the environments (item-factory).
+    2. Setup parameters for the agent training (SB3: PPO) and save metrics.
+        Run the training.
+    3. Save env and agent for later analysis.
+    4. Load the agent from drive
+    5. Rendering the env with a run of the trained agent.
+    6. Plot metrics 
+"""
+
+if __name__ == '__main__':
+    #########################################################
+    # 0. Setup I/O Paths
+    # Define some general parameters
+    train_steps = 1e6
+    n_seeds = 3
+    model_class = sb3.PPO
+    env_class = ItemFactory
+
+    env_params_json = 'env_params.json'
+
+    # Define a global studi save path
+    start_time = int(time.time())
+    study_root_path = Path(__file__).parent.parent / 'study_out' / f'{Path(__file__).stem}_{start_time}'
+    # Create an identifier, which is unique for every combination and easy to read in filesystem
+    identifier = f'{model_class.__name__}_{env_class.__name__}_{start_time}'
+    exp_path = study_root_path / identifier
+
+    #########################################################
+    # 1. Setup parameters for the environments (item-factory).
+    #
+    # Define property object parameters.
+    #  'ObservationProperties' are for specifying how the agent sees the env.
+    obs_props = ObservationProperties(render_agents=AgentRenderOptions.NOT,  # Agents won`t be shown in the obs at all
+                                      omit_agent_self=True,                  # This is default
+                                      additional_agent_placeholder=None,     # We will not take care of future agents
+                                      frames_to_stack=3,                     # To give the agent a notion of time
+                                      pomdp_r=2                              # the agents view-radius
+                                      )
+    #  'MovementProperties' are for specifying how the agent is allowed to move in the env.
+    move_props = MovementProperties(allow_diagonal_movement=True,   # Euclidean style (vertices)
+                                    allow_square_movement=True,     # Manhattan (edges)
+                                    allow_no_op=False)              # Pause movement (do nothing)
+
+    # 'ItemProperties' control if and how item is spawned
+    # TODO: Comments
+    item_props = ItemProperties(
+        n_items                      = 7,     # How many items are there at the same time
+        spawn_frequency              = 50,     # Spawn Frequency in Steps
+        n_drop_off_locations         = 10,     # How many DropOff locations are there at the same time
+        max_dropoff_storage_size     = 0,     # How many items are needed until the dropoff is full
+        max_agent_inventory_capacity = 5,     # How many items are needed until the agent inventory is full)
+        )
+
+    #  These are the EnvKwargs for initializing the env class, holding all former parameter-classes
+    # TODO: Comments
+    factory_kwargs = dict(n_agents=1,
+                          max_steps=400,
+                          parse_doors=True,
+                          level_name='rooms',
+                          doors_have_area=True,  #
+                          verbose=False,
+                          mv_prop=move_props,    # See Above
+                          obs_prop=obs_props,    # See Above
+                          done_at_collision=True,
+                          item_prop=item_props
+                          )
+
+    #########################################################
+    # 2. Setup parameters for the agent training (SB3: PPO) and save metrics.
+    agent_kwargs = dict()
+
+    #########################################################
+    # Run the Training
+    for seed in range(n_seeds):
+        # Make a copy if you want to alter things in the training loop; like the seed.
+        env_kwargs = factory_kwargs.copy()
+        env_kwargs.update(env_seed=seed)
+
+        # Output folder
+        seed_path = exp_path / f'{str(seed)}_{identifier}'
+        seed_path.mkdir(parents=True, exist_ok=True)
+
+        # Parameter Storage
+        param_path = seed_path / env_params_json
+        # Observation (measures) Storage
+        monitor_path = seed_path / 'monitor.pick'
+        recorder_path = seed_path / 'recorder.json'
+        # Model save Path for the trained model
+        model_save_path = seed_path / f'model.zip'
+
+        # Env Init & Model kwargs definition
+        with ItemFactory(**env_kwargs) as env_factory:
+
+            # EnvMonitor Init
+            env_monitor_callback = EnvMonitor(env_factory)
+
+            # EnvRecorder Init
+            env_recorder_callback = EnvRecorder(env_factory, freq=int(train_steps / 400 / 10))
+
+            # Model Init
+            model = model_class("MlpPolicy", env_factory,verbose=1, seed=seed, device='cpu')
+
+            # Model train
+            model.learn(total_timesteps=int(train_steps), callback=[env_monitor_callback, env_recorder_callback])
+
+            #########################################################
+            # 3. Save env and agent for later analysis.
+            #   Save the trained Model, the monitor (env measures) and the env parameters
+            model.named_observation_space = env_factory.named_observation_space
+            model.named_action_space = env_factory.named_action_space
+            model.save(model_save_path)
+            env_factory.save_params(param_path)
+            env_monitor_callback.save_run(monitor_path)
+            env_recorder_callback.save_records(recorder_path, save_occupation_map=False)
+
+    # Compare performance runs, for each seed within a model
+    try:
+        compare_seed_runs(exp_path, use_tex=False)
+    except ValueError:
+        pass
+
+    # Train ends here ############################################################
+
+    # Evaluation starts here #####################################################
+    # First Iterate over every model and monitor "as trained"
+    print('Start Measurement Tracking')
+    # For trained policy in study_root_path / identifier
+    for policy_path in [x for x in exp_path.iterdir() if x.is_dir()]:
+
+        # retrieve model class
+        model_cls = next(val for key, val in h.MODEL_MAP.items() if key in policy_path.parent.name)
+        # Load the agent agent
+        model = model_cls.load(policy_path / 'model.zip', device='cpu')
+        # Load old env kwargs
+        with next(policy_path.glob(env_params_json)).open('r') as f:
+            env_kwargs = simplejson.load(f)
+            # Make the env stop ar collisions
+            # (you only want to have a single collision per episode hence the statistics)
+            env_kwargs.update(done_at_collision=True)
+
+        # Init Env
+        with ItemFactory(**env_kwargs) as env_factory:
+            monitored_env_factory = EnvMonitor(env_factory)
+
+            # Evaluation Loop for i in range(n Episodes)
+            for episode in range(100):
+                # noinspection PyRedeclaration
+                env_state = monitored_env_factory.reset()
+                rew, done_bool = 0, False
+                while not done_bool:
+                    action = model.predict(env_state, deterministic=True)[0]
+                    env_state, step_r, done_bool, info_obj = monitored_env_factory.step(action)
+                    rew += step_r
+                    if done_bool:
+                        break
+                print(f'Factory run {episode} done, reward is:\n    {rew}')
+            monitored_env_factory.save_run(filepath=policy_path / 'eval_run_monitor.pick')
+    print('Measurements Done')

reload_agent.py

@@ -4,9 +4,9 @@ from pathlib import Path
 import yaml
 from stable_baselines3 import A2C, PPO, DQN
 
-from environments.factory.factory_dirt import Constants as c
+from environments.factory.additional.dirt.dirt_util import Constants
 
-from environments.factory.factory_dirt import DirtFactory
+from environments.factory.additional.dirt.factory_dirt import DirtFactory
 from environments.logging.envmonitor import EnvMonitor
 from environments.logging.recorder import EnvRecorder
 
@@ -23,7 +23,7 @@ if __name__ == '__main__':
     seed = 13
     n_agents = 1
     # out_path = Path('study_out/e_1_new_reward/no_obs/dirt/A2C_new_reward/0_A2C_new_reward')
-    out_path = Path('study_out/reload')
+    out_path = Path('quickstart/combinations/single_agent_train_dirt_env_1659374984/PPO_DirtFactory_1659374984/0_PPO_DirtFactory_1659374984/')
     model_path = out_path
 
     with (out_path / f'env_params.json').open('r') as f:
@@ -62,7 +62,7 @@ if __name__ == '__main__':
                 if render:
                     env.render()
                 try:
-                    door = next(x for x in env.unwrapped.unwrapped[c.DOORS] if x.is_open)
+                    door = next(x for x in env.unwrapped.unwrapped[Constants.DOORS] if x.is_open)
                     print('openDoor found')
                 except StopIteration:
                     pass

studies/e_1.py

@@ -19,9 +19,11 @@ import simplejson
 from stable_baselines3.common.vec_env import SubprocVecEnv
 
 from environments import helpers as h
-from environments.factory.factory_dirt import DirtProperties, DirtFactory
+from environments.factory.factory_dirt import DirtFactory
+from environments.factory.dirt_util import DirtProperties
 from environments.factory.combined_factories import DirtItemFactory
-from environments.factory.factory_item import ItemProperties, ItemFactory
+from environments.factory.factory_item import ItemFactory
+from environments.factory.additional.item.item_util import ItemProperties
 from environments.logging.envmonitor import EnvMonitor
 from environments.utility_classes import MovementProperties, ObservationProperties, AgentRenderOptions
 import pickle

studies/single_run_with_export.py

@@ -20,9 +20,12 @@ import simplejson
 from environments.helpers import ActionTranslator, ObservationTranslator
 from environments.logging.recorder import EnvRecorder
 from environments import helpers as h
-from environments.factory.factory_dirt import DirtProperties, DirtFactory
+from environments.factory.factory_dirt import DirtFactory
-from environments.factory.factory_item import ItemProperties, ItemFactory
+from environments.factory.dirt_util import DirtProperties
-from environments.factory.factory_dest import DestProperties, DestFactory, DestModeOptions
+from environments.factory.factory_item import ItemFactory
+from environments.factory.additional.item.item_util import ItemProperties
+from environments.factory.factory_dest import DestFactory
+from environments.factory.additional.dest.dest_util import DestModeOptions, DestProperties
 from environments.factory.combined_factories import DirtDestItemFactory
 from environments.logging.envmonitor import EnvMonitor
 from environments.utility_classes import MovementProperties, ObservationProperties, AgentRenderOptions