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Rework of Observations and Entity Differentiation, lazy obs build by notification

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This commit is contained in:
Steffen Illium 2021-12-22 10:48:36 +01:00
parent 7f7a3d9a3b
commit b43f595207
14 changed files with 961 additions and 487 deletions
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334
environments/factory/base/base_factory.py
View File

@ -1,7 +1,9 @@
import abc
import enum
import time
from collections import defaultdict
from enum import Enum
from itertools import chain
from pathlib import Path
from typing import List, Union, Iterable, Dict
import numpy as np
@ -14,7 +16,8 @@ from environments.factory.base.shadow_casting import Map
from environments.helpers import Constants as c, Constants
from environments import helpers as h
from environments.factory.base.objects import Agent, Tile, Action
from environments.factory.base.registers import Actions, Entities, Agents, Doors, FloorTiles, WallTiles, PlaceHolders
from environments.factory.base.registers import Actions, Entities, Agents, Doors, FloorTiles, WallTiles, PlaceHolders, \
GlobalPositions
from environments.utility_classes import MovementProperties, ObservationProperties, MarlFrameStack
from environments.utility_classes import AgentRenderOptions as a_obs
@ -30,17 +33,31 @@ class BaseFactory(gym.Env):
def action_space(self):
return spaces.Discrete(len(self._actions))
@property
def named_action_space(self):
return {x.identifier.value: idx for idx, x in enumerate(self._actions.values())}
@property
def observation_space(self):
if r := self._pomdp_r:
z = self._obs_cube.shape[0]
xy = r*2 + 1
level_shape = (z, xy, xy)
obs, _ = self._build_observations()
if self.n_agents > 1:
shape = obs[0].shape
else:
level_shape = self._obs_cube.shape
space = spaces.Box(low=0, high=1, shape=level_shape, dtype=np.float32)
shape = obs.shape
space = spaces.Box(low=0, high=1, shape=shape, dtype=np.float32)
return space
@property
def named_observation_space(self):
# Build it
_, named_obs = self._build_observations()
if self.n_agents > 1:
# Only return the first named obs space, as their structure at the moment is same.
return [{key.name: val for key, val in named_ob.items()} for named_ob in named_obs.values()][0]
else:
return {key.name: val for key, val in named_obs.items()}
@property
def pomdp_diameter(self):
return self._pomdp_r * 2 + 1
@ -86,11 +103,14 @@ class BaseFactory(gym.Env):
self.obs_prop = obs_prop
self.level_name = level_name
self._level_shape = None
self._obs_shape = None
self.verbose = verbose
self._renderer = None # expensive - don't use it when not required !
self._entities = Entities()
self.n_agents = n_agents
level_filepath = Path(__file__).parent.parent / h.LEVELS_DIR / f'{self.level_name}.txt'
self._parsed_level = h.parse_level(level_filepath)
self.max_steps = max_steps
self._pomdp_r = self.obs_prop.pomdp_r
@ -114,10 +134,12 @@ class BaseFactory(gym.Env):
# Objects
self._entities = Entities()
# Level
level_filepath = Path(__file__).parent.parent / h.LEVELS_DIR / f'{self.level_name}.txt'
parsed_level = h.parse_level(level_filepath)
level_array = h.one_hot_level(parsed_level)
level_array = h.one_hot_level(self._parsed_level)
level_array = np.pad(level_array, self.obs_prop.pomdp_r, 'constant', constant_values=1)
self._level_shape = level_array.shape
self._obs_shape = self._level_shape if not self.obs_prop.pomdp_r else (self.pomdp_diameter, ) * 2
# Walls
walls = WallTiles.from_argwhere_coordinates(
@ -134,13 +156,14 @@ class BaseFactory(gym.Env):
self._entities.register_additional_items({c.FLOOR: floor})
# NOPOS
self._NO_POS_TILE = Tile(c.NO_POS.value)
self._NO_POS_TILE = Tile(c.NO_POS.value, None)
# Doors
if self.parse_doors:
parsed_doors = h.one_hot_level(parsed_level, c.DOOR)
parsed_doors = h.one_hot_level(self._parsed_level, c.DOOR)
parsed_doors = np.pad(parsed_doors, self.obs_prop.pomdp_r, 'constant', constant_values=0)
if np.any(parsed_doors):
door_tiles = [floor.by_pos(pos) for pos in np.argwhere(parsed_doors == c.OCCUPIED_CELL.value)]
door_tiles = [floor.by_pos(tuple(pos)) for pos in np.argwhere(parsed_doors == c.OCCUPIED_CELL.value)]
doors = Doors.from_tiles(door_tiles, self._level_shape,
entity_kwargs=dict(context=floor)
)
@ -153,12 +176,11 @@ class BaseFactory(gym.Env):
# Agents
agents_to_spawn = self.n_agents-len(self._injected_agents)
agents_kwargs = dict(level_shape=self._level_shape,
individual_slices=self.obs_prop.render_agents == a_obs.SEPERATE,
hide_from_obs_builder=self.obs_prop.render_agents == a_obs.LEVEL,
is_observable=self.obs_prop.render_agents != a_obs.NOT)
agents_kwargs = dict(individual_slices=self.obs_prop.render_agents == a_obs.SEPERATE,
hide_from_obs_builder=self.obs_prop.render_agents in [a_obs.NOT, a_obs.LEVEL],
)
if agents_to_spawn:
agents = Agents.from_tiles(floor.empty_tiles[:agents_to_spawn], **agents_kwargs)
agents = Agents.from_tiles(floor.empty_tiles[:agents_to_spawn], self._level_shape, **agents_kwargs)
else:
agents = Agents(**agents_kwargs)
if self._injected_agents:
@ -173,10 +195,10 @@ class BaseFactory(gym.Env):
# TODO: Make this accept Lists for multiple placeholders
# Empty Observations with either [0, 1, N(0, 1)]
placeholder = PlaceHolders.from_tiles([self._NO_POS_TILE], self._level_shape,
entity_kwargs=dict(
fill_value=self.obs_prop.additional_agent_placeholder)
)
placeholder = PlaceHolders.from_values(self.obs_prop.additional_agent_placeholder, self._level_shape,
entity_kwargs=dict(
fill_value=self.obs_prop.additional_agent_placeholder)
)
self._entities.register_additional_items({c.AGENT_PLACEHOLDER: placeholder})
@ -184,24 +206,22 @@ class BaseFactory(gym.Env):
if additional_entities := self.additional_entities:
self._entities.register_additional_items(additional_entities)
if self.obs_prop.show_global_position_info:
global_positions = GlobalPositions(self._level_shape)
obs_shape_2d = self._level_shape if not self._pomdp_r else ((self.pomdp_diameter,) * 2)
global_positions.spawn_GlobalPositionObjects(obs_shape_2d, self[c.AGENT])
self._entities.register_additional_items({c.GLOBAL_POSITION: global_positions})
# Return
return self._entities
def _init_obs_cube(self):
arrays = self._entities.obs_arrays
obs_cube_z = sum([a.shape[0] if not self[key].is_per_agent else 1 for key, a in arrays.items()])
obs_cube_z += 1 if self.obs_prop.show_global_position_info else 0
self._obs_cube = np.zeros((obs_cube_z, *self._level_shape), dtype=np.float32)
def reset(self) -> (np.ndarray, int, bool, dict):
def reset(self) -> (np.typing.ArrayLike, int, bool, dict):
_ = self._base_init_env()
self._init_obs_cube()
self.do_additional_reset()
self._steps = 0
obs = self._get_observations()
obs, _ = self._build_observations()
return obs
def step(self, actions):
@ -264,7 +284,7 @@ class BaseFactory(gym.Env):
# Post step Hook for later use
info.update(self.hook_post_step())
obs = self._get_observations()
obs, _ = self._build_observations()
return obs, reward, done, info
@ -284,141 +304,120 @@ class BaseFactory(gym.Env):
else:
return c.NOT_VALID
def _get_observations(self) -> np.ndarray:
state_array_dict = self._entities.obs_arrays
if self.n_agents == 1:
obs = self._build_per_agent_obs(self[c.AGENT][0], state_array_dict)
elif self.n_agents >= 2:
obs = np.stack([self._build_per_agent_obs(agent, state_array_dict) for agent in self[c.AGENT]])
else:
raise ValueError('n_agents cannot be smaller than 1!!')
return obs
def _build_observations(self) -> np.typing.ArrayLike:
# Observation dict:
per_agent_expl_idx = dict()
per_agent_obsn = dict()
# Generel Observations
lvl_obs = self[c.WALLS].as_array()
door_obs = self[c.DOORS].as_array()
agent_obs = self[c.AGENT].as_array() if self.obs_prop.render_agents != a_obs.NOT else None
placeholder_obs = self[c.AGENT_PLACEHOLDER].as_array() if self[c.AGENT_PLACEHOLDER] else None
add_obs_dict = self._additional_observations()
def _build_per_agent_obs(self, agent: Agent, state_array_dict) -> np.ndarray:
agent_pos_is_omitted = False
agent_omit_idx = None
if self.obs_prop.omit_agent_self and self.n_agents == 1:
pass
elif self.obs_prop.omit_agent_self and self.obs_prop.render_agents in [a_obs.COMBINED, ] and self.n_agents > 1:
state_array_dict[c.AGENT][0, agent.x, agent.y] -= agent.encoding
agent_pos_is_omitted = True
elif self.obs_prop.omit_agent_self and self.obs_prop.render_agents == a_obs.SEPERATE and self.n_agents > 1:
agent_omit_idx = next((i for i, a in enumerate(self[c.AGENT]) if a == agent))
running_idx, shadowing_idxs, can_be_shadowed_idxs = 0, [], []
self._obs_cube[:] = 0
# FIXME: Refactor this! Make a globally build observation, then add individual per-agent-obs
for key, array in state_array_dict.items():
# Flush state array object representation to obs cube
if not self[key].hide_from_obs_builder:
if self[key].is_per_agent:
per_agent_idx = self[key].idx_by_entity(agent)
z = 1
self._obs_cube[running_idx: running_idx+z] = array[per_agent_idx]
else:
if key == c.AGENT and agent_omit_idx is not None:
z = array.shape[0] - 1
for array_idx in range(array.shape[0]):
self._obs_cube[running_idx: running_idx+z] = array[[x for x in range(array.shape[0])
if x != agent_omit_idx]]
# Agent OBS are combined
elif key == c.AGENT and self.obs_prop.omit_agent_self \
and self.obs_prop.render_agents == a_obs.COMBINED:
z = 1
self._obs_cube[running_idx: running_idx + z] = array
# Each Agent is rendered on a seperate array slice
for agent_idx, agent in enumerate(self[c.AGENT]):
obs_dict = dict()
# Build Agent Observations
if self.obs_prop.render_agents != a_obs.NOT:
if self.obs_prop.omit_agent_self:
if self.obs_prop.render_agents == a_obs.SEPERATE:
agent_obs = np.take(agent_obs, [x for x in range(self.n_agents) if x != agent_idx], axis=0)
else:
z = array.shape[0]
self._obs_cube[running_idx: running_idx + z] = array
# Define which OBS SLices cast a Shadow
if self[key].is_blocking_light:
for i in range(z):
shadowing_idxs.append(running_idx + i)
# Define which OBS SLices are effected by shadows
if self[key].can_be_shadowed:
for i in range(z):
can_be_shadowed_idxs.append(running_idx + i)
running_idx += z
agent_obs = agent_obs.copy()
agent_obs[(0, *agent.pos)] -= agent.encoding
if agent_pos_is_omitted:
state_array_dict[c.AGENT][0, agent.x, agent.y] += agent.encoding
if self._pomdp_r:
obs = self._do_pomdp_obs_cutout(agent, self._obs_cube)
else:
obs = self._obs_cube
obs = obs.copy()
if self.obs_prop.cast_shadows:
obs_block_light = [obs[idx] != c.OCCUPIED_CELL.value for idx in shadowing_idxs]
door_shadowing = False
if self.parse_doors:
if doors := self[c.DOORS]:
if door := doors.by_pos(agent.pos):
if door.is_closed:
for group in door.connectivity_subgroups:
if agent.last_pos not in group:
door_shadowing = True
if self._pomdp_r:
blocking = [tuple(np.subtract(x, agent.pos) + (self._pomdp_r, self._pomdp_r))
for x in group]
xs, ys = zip(*blocking)
else:
xs, ys = zip(*group)
# noinspection PyUnresolvedReferences
obs_block_light[0][xs, ys] = False
light_block_map = Map((np.prod(obs_block_light, axis=0) != True).astype(int))
if self._pomdp_r:
light_block_map = light_block_map.do_fov(self._pomdp_r, self._pomdp_r, max(self._level_shape))
else:
light_block_map = light_block_map.do_fov(*agent.pos, max(self._level_shape))
if door_shadowing:
# noinspection PyUnboundLocalVariable
light_block_map[xs, ys] = 0
agent.temp_light_map = light_block_map
for obs_idx in can_be_shadowed_idxs:
obs[obs_idx] = ((obs[obs_idx] * light_block_map) + 0.) - (1 - light_block_map) # * obs[0])
else:
pass
# Agents observe other agents as wall
if self.obs_prop.render_agents == a_obs.LEVEL and self.n_agents > 1:
other_agent_obs = self[c.AGENT].as_array()
if self.obs_prop.omit_agent_self:
other_agent_obs[:, agent.x, agent.y] -= agent.encoding
# Build Level Observations
if self.obs_prop.render_agents == a_obs.LEVEL:
lvl_obs = lvl_obs.copy()
lvl_obs += agent_obs
obs_dict[c.WALLS] = lvl_obs
if self.obs_prop.render_agents in [a_obs.SEPERATE, a_obs.COMBINED]:
obs_dict[c.AGENT] = agent_obs
if self[c.AGENT_PLACEHOLDER]:
obs_dict[c.AGENT_PLACEHOLDER] = placeholder_obs
obs_dict[c.DOORS] = door_obs
obs_dict.update(add_obs_dict)
observations = np.vstack(list(obs_dict.values()))
if self.obs_prop.pomdp_r:
oobs = self._do_pomdp_obs_cutout(agent, other_agent_obs)[0]
# noinspection PyUnresolvedReferences
mask = (oobs != c.SHADOWED_CELL.value).astype(int)
obs[0] += oobs * mask
observations = self._do_pomdp_cutout(agent, observations)
raw_obs = self._additional_raw_observations(agent)
observations = np.vstack((observations, *list(raw_obs.values())))
keys = list(chain(obs_dict.keys(), raw_obs.keys()))
idxs = np.cumsum([x.shape[0] for x in chain(obs_dict.values(), raw_obs.values())]) - 1
per_agent_expl_idx[agent.name] = {key: list(range(a, b)) for key, a, b in
zip(keys, idxs, list(idxs[1:]) + [idxs[-1]+1, ])}
# Shadow Casting
try:
light_block_obs = [obs_idx for key, obs_idx in per_agent_expl_idx[agent.name].items()
if self[key].is_blocking_light]
# Flatten
light_block_obs = [x for y in light_block_obs for x in y]
shadowed_obs = [obs_idx for key, obs_idx in per_agent_expl_idx[agent.name].items()
if self[key].can_be_shadowed]
# Flatten
shadowed_obs = [x for y in shadowed_obs for x in y]
except AttributeError as e:
print('Check your Keys! Only use Constants as Keys!')
print(e)
raise e
if self.obs_prop.cast_shadows:
obs_block_light = observations[light_block_obs] != c.OCCUPIED_CELL.value
door_shadowing = False
if self.parse_doors:
if doors := self[c.DOORS]:
if door := doors.by_pos(agent.pos):
if door.is_closed:
for group in door.connectivity_subgroups:
if agent.last_pos not in group:
door_shadowing = True
if self._pomdp_r:
blocking = [
tuple(np.subtract(x, agent.pos) + (self._pomdp_r, self._pomdp_r))
for x in group]
xs, ys = zip(*blocking)
else:
xs, ys = zip(*group)
# noinspection PyUnresolvedReferences
obs_block_light[:, xs, ys] = False
light_block_map = Map((np.prod(obs_block_light, axis=0) != True).astype(int).squeeze())
if self._pomdp_r:
light_block_map = light_block_map.do_fov(self._pomdp_r, self._pomdp_r, max(self._level_shape))
else:
light_block_map = light_block_map.do_fov(*agent.pos, max(self._level_shape))
if door_shadowing:
# noinspection PyUnboundLocalVariable
light_block_map[xs, ys] = 0
agent.temp_light_map = light_block_map.copy()
observations[shadowed_obs] = ((observations[shadowed_obs] * light_block_map) + 0.) - (1 - light_block_map)
else:
obs[0] += other_agent_obs
pass
# Additional Observation:
for additional_obs in self.additional_obs_build():
obs[running_idx:running_idx+additional_obs.shape[0]] = additional_obs
running_idx += additional_obs.shape[0]
for additional_per_agent_obs in self.additional_per_agent_obs_build(agent):
obs[running_idx:running_idx + additional_per_agent_obs.shape[0]] = additional_per_agent_obs
running_idx += additional_per_agent_obs.shape[0]
per_agent_obsn[agent.name] = observations
return obs
if self.n_agents == 1:
agent_name = self[c.AGENT][0].name
obs, explained_idx = per_agent_obsn[agent_name], per_agent_expl_idx[agent_name]
elif self.n_agents >= 2:
obs, explained_idx = np.stack(list(per_agent_obsn.values())), per_agent_expl_idx
else:
raise ValueError
def _do_pomdp_obs_cutout(self, agent, obs_to_be_padded):
return obs, explained_idx
def _do_pomdp_cutout(self, agent, obs_to_be_padded):
assert obs_to_be_padded.ndim == 3
r, d = self._pomdp_r, self.pomdp_diameter
x0, x1 = max(0, agent.x - r), min(agent.x + r + 1, self._level_shape[0])
y0, y1 = max(0, agent.y - r), min(agent.y + r + 1, self._level_shape[1])
# Other Agent Obs = oobs
oobs = obs_to_be_padded[:, x0:x1, y0:y1]
if oobs.shape[0:] != (d, d):
if oobs.shape[1:] != (d, d):
if xd := oobs.shape[1] % d:
if agent.x > r:
x0_pad = 0
@ -478,7 +477,7 @@ class BaseFactory(gym.Env):
if doors := self[c.DOORS]:
if self.doors_have_area:
if door := doors.by_pos(new_tile.pos):
if door.can_collide:
if door.is_open:
return agent.tile, c.NOT_VALID
else: # door.is_closed:
pass
@ -569,7 +568,7 @@ class BaseFactory(gym.Env):
if not self._renderer: # lazy init
from environments.factory.base.renderer import Renderer, RenderEntity
global Renderer, RenderEntity
height, width = self._obs_cube.shape[1:]
height, width = self._level_shape
self._renderer = Renderer(width, height, view_radius=self._pomdp_r, fps=5)
# noinspection PyUnboundLocalVariable
@ -636,20 +635,6 @@ class BaseFactory(gym.Env):
# Functions which provide additions to functions of the base class
# Always call super!!!!!!
@abc.abstractmethod
def additional_obs_build(self) -> List[np.ndarray]:
return []
def additional_per_agent_obs_build(self, agent) -> List[np.ndarray]:
additional_per_agent_obs = []
if self.obs_prop.show_global_position_info:
pos_array = np.zeros(self.observation_space.shape[1:])
for xy in range(1):
pos_array[0, xy] = agent.pos[xy] / self._level_shape[xy]
additional_per_agent_obs.append(pos_array)
return additional_per_agent_obs
@abc.abstractmethod
def do_additional_reset(self) -> None:
pass
@ -666,6 +651,17 @@ class BaseFactory(gym.Env):
def check_additional_done(self) -> bool:
return False
@abc.abstractmethod
def _additional_observations(self) -> Dict[Constants, np.typing.ArrayLike]:
return {}
@abc.abstractmethod
def _additional_raw_observations(self, agent) -> Dict[Constants, np.typing.ArrayLike]:
additional_raw_observations = {}
if self.obs_prop.show_global_position_info:
additional_raw_observations.update({c.GLOBAL_POSITION: self[c.GLOBAL_POSITION].by_entity(agent).as_array()})
return additional_raw_observations
@abc.abstractmethod
def calculate_additional_reward(self, agent: Agent) -> (int, dict):
return 0, {}

128
environments/factory/base/objects.py
View File

@ -3,22 +3,26 @@ from enum import Enum
from typing import Union
import networkx as nx
import numpy as np
from environments import helpers as h
from environments.helpers import Constants as c
import itertools
##########################################################################
# ##################### Base Object Definition ######################### #
##########################################################################
class Object:
"""Generell Objects for Organisation and Maintanance such as Actions etc..."""
_u_idx = defaultdict(lambda: 0)
def __bool__(self):
return True
@property
def is_blocking_light(self):
return self._is_blocking_light
@property
def name(self):
return self._name
@ -43,7 +47,7 @@ class Object:
elif self._str_ident is not None and self._enum_ident is None:
self._name = f'{self.__class__.__name__}[{self._str_ident}]'
elif self._str_ident is None and self._enum_ident is None:
self._name = f'{self.__class__.__name__}#{self._u_idx[self.__class__.__name__]}'
self._name = f'{self.__class__.__name__}#{Object._u_idx[self.__class__.__name__]}'
Object._u_idx[self.__class__.__name__] += 1
else:
raise ValueError('Please use either of the idents.')
@ -68,16 +72,56 @@ class Object:
return other.name == self.name
class Entity(Object):
class EnvObject(Object):
@property
def can_collide(self):
return True
"""Objects that hold Information that are observable, but have no position on the env grid. Inventories etc..."""
_u_idx = defaultdict(lambda: 0)
@property
def encoding(self):
return c.OCCUPIED_CELL.value
def __init__(self, register, **kwargs):
super(EnvObject, self).__init__(**kwargs)
self._register = register
class BoundingMixin:
@property
def bound_entity(self):
return self._bound_entity
def __init__(self, entity_to_be_bound, *args, **kwargs):
super().__init__(*args, **kwargs)
assert entity_to_be_bound is not None
self._bound_entity = entity_to_be_bound
def __repr__(self):
s = super(BoundingMixin, self).__repr__()
i = s[:s.find('(')]
return f'{s[:i]}[{self.bound_entity.name}]{s[i:]}'
@property
def name(self):
return f'{super(BoundingMixin, self).name}({self._bound_entity.name})'
def belongs_to_entity(self, entity):
return entity == self.bound_entity
class Entity(EnvObject):
"""Full Env Entity that lives on the env Grid. Doors, Items, Dirt etc..."""
@property
def is_blocking_light(self):
return self._is_blocking_light
@property
def can_collide(self):
return True
@property
def x(self):
return self.pos[0]
@ -94,9 +138,10 @@ class Entity(Object):
def tile(self):
return self._tile
def __init__(self, tile, **kwargs):
super().__init__(**kwargs)
def __init__(self, tile, *args, is_blocking_light=True, **kwargs):
super().__init__(*args, **kwargs)
self._tile = tile
self._is_blocking_light = is_blocking_light
tile.enter(self)
def summarize_state(self, **_) -> dict:
@ -104,7 +149,7 @@ class Entity(Object):
tile=str(self.tile.name), can_collide=bool(self.can_collide))
def __repr__(self):
return f'{self.name}(@{self.pos})'
return super(Entity, self).__repr__() + f'(@{self.pos})'
class MoveableEntity(Entity):
@ -118,7 +163,7 @@ class MoveableEntity(Entity):
if self._last_tile:
return self._last_tile.pos
else:
return c.NO_POS
return c.NO_POS.value
@property
def direction_of_view(self):
@ -137,45 +182,66 @@ class MoveableEntity(Entity):
curr_tile.leave(self)
self._tile = next_tile
self._last_tile = curr_tile
self._register.notify_change_to_value(self)
return True
else:
return False
##########################################################################
# ####################### Objects and Entitys ########################## #
##########################################################################
class Action(Object):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
class PlaceHolder(MoveableEntity):
class PlaceHolder(Object):
def __init__(self, *args, fill_value=0, **kwargs):
super().__init__(*args, **kwargs)
self._fill_value = fill_value
@property
def last_tile(self):
return self.tile
@property
def direction_of_view(self):
return self.pos
@property
def can_collide(self):
return False
@property
def encoding(self):
return c.NO_POS.value[0]
return self._fill_value
@property
def name(self):
return "PlaceHolder"
class Tile(Object):
class GlobalPosition(EnvObject):
def belongs_to_entity(self, entity):
return self._agent == entity
def __init__(self, level_shape, obs_shape, agent, normalized: bool = True):
super(GlobalPosition, self).__init__(self)
self._obs_shape = (1, *obs_shape) if len(obs_shape) == 2 else obs_shape
self._agent = agent
self._level_shape = level_shape
self._normalized = normalized
def as_array(self):
pos_array = np.zeros(self._obs_shape)
for xy in range(1):
pos_array[0, 0, xy] = self._agent.pos[xy] / self._level_shape[xy]
return pos_array
class Tile(EnvObject):
@property
def encoding(self):
return c.FREE_CELL.value
@property
def guests_that_can_collide(self):
@ -197,8 +263,8 @@ class Tile(Object):
def pos(self):
return self._pos
def __init__(self, pos, **kwargs):
super(Tile, self).__init__(**kwargs)
def __init__(self, pos, *args, **kwargs):
super(Tile, self).__init__(*args, **kwargs)
self._guests = dict()
self._pos = tuple(pos)
@ -233,6 +299,11 @@ class Tile(Object):
class Wall(Tile):
@property
def encoding(self):
return c.OCCUPIED_CELL.value
pass
@ -247,7 +318,8 @@ class Door(Entity):
@property
def encoding(self):
return 1 if self.is_closed else 2
# This is important as it shadow is checked by occupation value
return c.OCCUPIED_CELL.value if self.is_closed else 2
@property
def str_state(self):
@ -307,11 +379,13 @@ class Door(Entity):
def _open(self):
self.connectivity.add_edges_from([(self.pos, x) for x in range(len(self.connectivity_subgroups))])
self._state = c.OPEN_DOOR
self._register.notify_change_to_value(self)
self.time_to_close = self.auto_close_interval
def _close(self):
self.connectivity.remove_node(self.pos)
self._state = c.CLOSED_DOOR
self._register.notify_change_to_value(self)
def is_linked(self, old_pos, new_pos):
try:

315
environments/factory/base/registers.py
View File

@ -1,18 +1,23 @@
import numbers
import random
from abc import ABC
from typing import List, Union, Dict
from typing import List, Union, Dict, Tuple
import numpy as np
from environments.factory.base.objects import Entity, Tile, Agent, Door, Action, Wall, Object, PlaceHolder
from environments.factory.base.objects import Entity, Tile, Agent, Door, Action, Wall, PlaceHolder, GlobalPosition, \
Object, EnvObject
from environments.utility_classes import MovementProperties
from environments import helpers as h
from environments.helpers import Constants as c
##########################################################################
# ##################### Base Register Definition ####################### #
##########################################################################
class Register:
_accepted_objects = Entity
class ObjectRegister:
_accepted_objects = Object
@property
def name(self):
@ -48,6 +53,12 @@ class Register:
def items(self):
return self._register.items()
def _get_index(self, item):
try:
return next(i for i, v in enumerate(self._register.values()) if v == item)
except (StopIteration, AssertionError):
return None
def __getitem__(self, item):
if isinstance(item, (int, np.int64, np.int32)):
if item < 0:
@ -65,39 +76,66 @@ class Register:
return f'{self.__class__.__name__}({self._register})'
class ObjectRegister(Register):
class EnvObjectRegister(ObjectRegister):
hide_from_obs_builder = False
_accepted_objects = EnvObject
def __init__(self, level_shape: (int, int), *args, individual_slices=False, is_per_agent=False, **kwargs):
super(ObjectRegister, self).__init__(*args, **kwargs)
self.is_per_agent = is_per_agent
self.individual_slices = individual_slices
self._level_shape = level_shape
def __init__(self, obs_shape: (int, int), *args, **kwargs):
super(EnvObjectRegister, self).__init__(*args, **kwargs)
self._shape = obs_shape
self._array = None
self.hide_from_obs_builder = False
self._lazy_eval_transforms = []
def register_item(self, other):
super(ObjectRegister, self).register_item(other)
def register_item(self, other: EnvObject):
super(EnvObjectRegister, self).register_item(other)
if self._array is None:
self._array = np.zeros((1, *self._level_shape))
else:
if self.individual_slices:
self._array = np.concatenate((self._array, np.zeros((1, *self._array.shape[1:]))))
self._array = np.zeros((1, *self._shape))
self.notify_change_to_value(other)
def as_array(self):
if self._lazy_eval_transforms:
idxs, values = zip(*self._lazy_eval_transforms)
# nuumpy put repects the ordering so that
np.put(self._array, idxs, values)
self._lazy_eval_transforms = []
return self._array
def summarize_states(self, n_steps=None):
return [val.summarize_state(n_steps=n_steps) for val in self.values()]
def notify_change_to_free(self, env_object: EnvObject):
self._array_change_notifyer(env_object, value=c.FREE_CELL.value)
class EntityObjectRegister(ObjectRegister, ABC):
def notify_change_to_value(self, env_object: EnvObject):
self._array_change_notifyer(env_object)
def as_array(self):
raise NotImplementedError
def _array_change_notifyer(self, env_object: EnvObject, value=None):
pos = self._get_index(env_object)
value = value if value is not None else env_object.encoding
self._lazy_eval_transforms.append((pos, value))
def __delitem__(self, name):
self.notify_change_to_free(self._register[name])
del self._register[name]
def delete_env_object(self, env_object: EnvObject):
del self[env_object.name]
def delete_env_object_by_name(self, name):
del self[name]
class EntityRegister(EnvObjectRegister, ABC):
_accepted_objects = Entity
@classmethod
def from_tiles(cls, tiles, *args, entity_kwargs=None, **kwargs):
# objects_name = cls._accepted_objects.__name__
register_obj = cls(*args, **kwargs)
entities = [cls._accepted_objects(tile, str_ident=i, **entity_kwargs if entity_kwargs is not None else {})
entities = [cls._accepted_objects(tile, register_obj, str_ident=i,
**entity_kwargs if entity_kwargs is not None else {})
for i, tile in enumerate(tiles)]
register_obj.register_additional_items(entities)
return register_obj
@ -115,86 +153,172 @@ class EntityObjectRegister(ObjectRegister, ABC):
def tiles(self):
return [entity.tile for entity in self]
def __init__(self, *args, is_blocking_light=False, is_observable=True, can_be_shadowed=True, **kwargs):
super(EntityObjectRegister, self).__init__(*args, **kwargs)
self.can_be_shadowed = can_be_shadowed
self.is_blocking_light = is_blocking_light
self.is_observable = is_observable
@property
def encodings(self):
return [x.encoding for x in self]
def by_pos(self, pos):
if isinstance(pos, np.ndarray):
pos = tuple(pos)
def __init__(self, level_shape, *args,
is_blocking_light: bool = False,
can_be_shadowed: bool = True,
individual_slices: bool = False, **kwargs):
super(EntityRegister, self).__init__(level_shape, *args, **kwargs)
self._lazy_eval_transforms = []
self.can_be_shadowed = can_be_shadowed
self.individual_slices = individual_slices
self.is_blocking_light = is_blocking_light
def __delitem__(self, name):
idx, obj = next((i, obj) for i, obj in enumerate(self) if obj.name == name)
obj.tile.leave(obj)
super(EntityRegister, self).__delitem__(name)
if self.individual_slices:
self._array = np.delete(self._array, idx, axis=0)
def as_array(self):
if self._lazy_eval_transforms:
idxs, values = zip(*self._lazy_eval_transforms)
# numpy put repects the ordering so that
# Todo: Export the index building in a seperate function
np.put(self._array, [np.ravel_multi_index(idx, self._array.shape) for idx in idxs], values)
self._lazy_eval_transforms = []
return self._array
def _array_change_notifyer(self, entity, pos=None, value=None):
# Todo: Export the contruction in a seperate function
pos = pos if pos is not None else entity.pos
value = value if value is not None else entity.encoding
x, y = pos
if self.individual_slices:
idx = (self._get_index(entity), x, y)
else:
idx = (0, x, y)
self._lazy_eval_transforms.append((idx, value))
def by_pos(self, pos: Tuple[int, int]):
try:
return next(item for item in self.values() if item.pos == pos)
return next(item for item in self if item.pos == tuple(pos))
except StopIteration:
return None
class MovingEntityObjectRegister(EntityObjectRegister, ABC):
class BoundRegisterMixin(EnvObjectRegister, ABC):
@classmethod
def from_entities_to_bind(self, entitites):
def from_values(cls, values: Union[str, numbers.Number, List[Union[str, numbers.Number]]],
*args, object_kwargs=None, **kwargs):
# objects_name = cls._accepted_objects.__name__
if isinstance(values, (str, numbers.Number)):
values = [values]
register_obj = cls(*args, **kwargs)
objects = [cls._accepted_objects(register_obj, str_ident=i, fill_value=value,
**object_kwargs if object_kwargs is not None else {})
for i, value in enumerate(values)]
register_obj.register_additional_items(objects)
return register_obj
class MovingEntityObjectRegister(EntityRegister, ABC):
def __init__(self, *args, **kwargs):
super(MovingEntityObjectRegister, self).__init__(*args, **kwargs)
def by_pos(self, pos):
if isinstance(pos, np.ndarray):
pos = tuple(pos)
def notify_change_to_value(self, entity):
super(MovingEntityObjectRegister, self).notify_change_to_value(entity)
if entity.last_pos != c.NO_POS.value:
try:
self._array_change_notifyer(entity, entity.last_pos, value=c.FREE_CELL.value)
except AttributeError:
pass
##########################################################################
# ################# Objects and Entity Registers ####################### #
##########################################################################
class GlobalPositions(EnvObjectRegister):
_accepted_objects = GlobalPosition
is_blocking_light = False
can_be_shadowed = False
hide_from_obs_builder = True
def __init__(self, *args, **kwargs):
super(GlobalPositions, self).__init__(*args, is_per_agent=True, individual_slices=True, **kwargs)
def as_array(self):
# Todo make this lazy?
return np.stack([gp.as_array() for inv_idx, gp in enumerate(self)])
def spawn_GlobalPositionObjects(self, obs_shape, agents):
global_positions = [self._accepted_objects(self._shape, obs_shape, agent)
for _, agent in enumerate(agents)]
# noinspection PyTypeChecker
self.register_additional_items(global_positions)
def summarize_states(self, n_steps=None):
return {}
def idx_by_entity(self, entity):
try:
return next(x for x in self if x.pos == pos)
return next((idx for idx, inv in enumerate(self) if inv.belongs_to_entity(entity)))
except StopIteration:
return None
def __delitem__(self, name):
idx = next(i for i, entity in enumerate(self) if entity.name == name)
del self._register[name]
if self.individual_slices:
self._array = np.delete(self._array, idx, axis=0)
def delete_entity(self, item):
self.delete_entity_by_name(item.name)
def delete_entity_by_name(self, name):
del self[name]
def by_entity(self, entity):
try:
return next((inv for inv in self if inv.belongs_to_entity(entity)))
except StopIteration:
return None
class PlaceHolders(MovingEntityObjectRegister):
class PlaceHolders(EnvObjectRegister):
_accepted_objects = PlaceHolder
def __init__(self, *args, fill_value: Union[str, int] = 0, **kwargs):
def __init__(self, *args, **kwargs):
assert not 'individual_slices' in kwargs, 'Keyword - "individual_slices": "True" and must not be altered'
kwargs.update(individual_slices=False)
super().__init__(*args, **kwargs)
self.fill_value = fill_value
@classmethod
def from_values(cls, values: Union[str, numbers.Number, List[Union[str, numbers.Number]]],
*args, object_kwargs=None, **kwargs):
# objects_name = cls._accepted_objects.__name__
if isinstance(values, (str, numbers.Number)):
values = [values]
register_obj = cls(*args, **kwargs)
objects = [cls._accepted_objects(register_obj, str_ident=i, fill_value=value,
**object_kwargs if object_kwargs is not None else {})
for i, value in enumerate(values)]
register_obj.register_additional_items(objects)
return register_obj
# noinspection DuplicatedCode
def as_array(self):
if isinstance(self.fill_value, numbers.Number):
self._array[:] = self.fill_value
elif isinstance(self.fill_value, str):
if self.fill_value.lower() in ['normal', 'n']:
self._array = np.random.normal(size=self._array.shape)
for idx, placeholder in enumerate(self):
if isinstance(placeholder.encoding, numbers.Number):
self._array[idx][:] = placeholder.fill_value
elif isinstance(placeholder.fill_value, str):
if placeholder.fill_value.lower() in ['normal', 'n']:
self._array[:] = np.random.normal(size=self._array.shape)
else:
raise ValueError('Choose one of: ["normal", "N"]')
else:
raise ValueError('Choose one of: ["normal", "N"]')
else:
raise TypeError('Objects of type "str" or "number" is required here.')
raise TypeError('Objects of type "str" or "number" is required here.')
if self.individual_slices:
return self._array
else:
return self._array[None, 0]
return self._array
class Entities(Register):
_accepted_objects = EntityObjectRegister
class Entities(ObjectRegister):
_accepted_objects = EntityRegister
@property
def observable_arrays(self):
# FIXME: Find a better name
return {key: val.as_array() for key, val in self.items() if val.is_observable}
def arrays(self):
return {key: val.as_array() for key, val in self.items()}
@property
def obs_arrays(self):
# FIXME: Find a better name
return {key: val.as_array() for key, val in self.items() if val.is_observable and not val.hide_from_obs_builder}
return {key: val.as_array() for key, val in self.items() if not val.hide_from_obs_builder}
@property
def names(self):
@ -220,34 +344,34 @@ class Entities(Register):
return found_entities
class WallTiles(EntityObjectRegister):
class WallTiles(EntityRegister):
_accepted_objects = Wall
_light_blocking = True
hide_from_obs_builder = True
def as_array(self):
if not np.any(self._array):
# Which is Faster?
# indices = [x.pos for x in self]
# np.put(self._array, [np.ravel_multi_index((0, *x), self._array.shape) for x in indices], self.encodings)
x, y = zip(*[x.pos for x in self])
self._array[0, x, y] = self.encoding
return self._array
def __init__(self, *args, **kwargs):
super(WallTiles, self).__init__(*args, individual_slices=False,
is_blocking_light=self._light_blocking, **kwargs)
super(WallTiles, self).__init__(*args, is_blocking_light=self._light_blocking, individual_slices=False,
**kwargs)
@property
def encoding(self):
return c.OCCUPIED_CELL.value
@property
def array(self):
return self._array
@classmethod
def from_argwhere_coordinates(cls, argwhere_coordinates, *args, **kwargs):
tiles = cls(*args, **kwargs)
# noinspection PyTypeChecker
tiles.register_additional_items(
[cls._accepted_objects(pos, is_blocking_light=cls._light_blocking)
[cls._accepted_objects(pos, tiles, is_blocking_light=cls._light_blocking)
for pos in argwhere_coordinates]
)
return tiles
@ -264,12 +388,11 @@ class WallTiles(EntityObjectRegister):
class FloorTiles(WallTiles):
_accepted_objects = Tile
_light_blocking = False
def __init__(self, *args, **kwargs):
super(FloorTiles, self).__init__(*args, is_observable=False, **kwargs)
super(FloorTiles, self).__init__(*args, **kwargs)
@property
def encoding(self):
@ -297,22 +420,21 @@ class FloorTiles(WallTiles):
class Agents(MovingEntityObjectRegister):
_accepted_objects = Agent
def __init__(self, *args, hide_from_obs_builder=False, **kwargs):
super().__init__(*args, **kwargs)
self.hide_from_obs_builder = hide_from_obs_builder
# noinspection DuplicatedCode
def as_array(self):
self._array[:] = c.FREE_CELL.value
# noinspection PyTupleAssignmentBalance
for z, x, y, v in zip(range(len(self)), *zip(*[x.pos for x in self]), [x.encoding for x in self]):
if self.individual_slices:
self._array[z, x, y] += v
else:
self._array[0, x, y] += v
@DeprecationWarning
def Xas_array(self):
# Super Safe Version
# self._array[:] = c.FREE_CELL.value
indices = list(zip(range(len(self)), *zip(*[x.last_pos for x in self])))
np.put(self._array, [np.ravel_multi_index(x, self._array.shape) for x in indices], c.FREE_CELL.value)
indices = list(zip(range(len(self)), *zip(*[x.pos for x in self])))
np.put(self._array, [np.ravel_multi_index(x, self._array.shape) for x in indices], self.encodings)
if self.individual_slices:
return self._array
else:
@ -329,17 +451,11 @@ class Agents(MovingEntityObjectRegister):
self._register[agent.name] = agent
class Doors(EntityObjectRegister):
class Doors(EntityRegister):
def __init__(self, *args, **kwargs):
super(Doors, self).__init__(*args, is_blocking_light=True, **kwargs)
def as_array(self):
self._array[:] = 0
for door in self:
self._array[0, door.x, door.y] = door.encoding
return self._array
_accepted_objects = Door
def get_near_position(self, position: (int, int)) -> Union[None, Door]:
@ -353,8 +469,7 @@ class Doors(EntityObjectRegister):
door.tick()
class Actions(Register):
class Actions(ObjectRegister):
_accepted_objects = Action
@property
@ -385,7 +500,7 @@ class Actions(Register):
return action in self.movement_actions.values()
class Zones(Register):
class Zones(ObjectRegister):
@property
def accounting_zones(self):

2
environments/factory/base/shadow_casting.py
View File

@ -13,7 +13,7 @@ mult_array = np.asarray([
class Map(object):
# Multipliers for transforming coordinates to other octants:
def __init__(self, map_array: np.ndarray, diamond_slope: float = 0.9):
def __init__(self, map_array: np.typing.ArrayLike, diamond_slope: float = 0.9):
self.data = map_array
self.width, self.height = map_array.shape
self.light = np.full_like(self.data, c.FREE_CELL.value)

7
environments/factory/combined_factories.py
View File

@ -1,6 +1,7 @@
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
@ -17,6 +18,12 @@ class DirtBatteryFactory(DirtFactory, BatteryFactory):
super().__init__(*args, **kwargs)
# noinspection PyAbstractClass
class DirtDestItemFactory(ItemFactory, DirtFactory, DestFactory):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
if __name__ == '__main__':
from environments.utility_classes import AgentRenderOptions as ARO, ObservationProperties

80
environments/factory/factory_battery.py
View File

@ -1,18 +1,18 @@
from typing import Union, NamedTuple
from typing import Union, NamedTuple, Dict
import numpy as np
from environments.factory.base.base_factory import BaseFactory
from environments.factory.base.objects import Agent, Action, Entity
from environments.factory.base.registers import EntityObjectRegister, ObjectRegister
from environments.factory.base.objects import Agent, Action, Entity, EnvObject, BoundingMixin
from environments.factory.base.registers import EntityRegister, EnvObjectRegister
from environments.factory.base.renderer import RenderEntity
from environments.helpers import Constants as c
from environments.helpers import Constants as c, Constants
from environments import helpers as h
CHARGE_ACTION = h.EnvActions.CHARGE
ITEM_DROP_OFF = 1
CHARGE_POD = 1
class BatteryProperties(NamedTuple):
@ -24,42 +24,18 @@ class BatteryProperties(NamedTuple):
multi_charge: bool = False
class Battery(object):
class Battery(EnvObject, BoundingMixin):
@property
def is_discharged(self):
return self.charge_level == 0
@property
def is_blocking_light(self):
return False
@property
def can_collide(self):
return False
@property
def name(self):
return f'{self.__class__.__name__}({self.agent.name})'
def __init__(self, pomdp_r: int, level_shape: (int, int), agent: Agent, initial_charge_level: float):
super().__init__()
self.agent = agent
self._pomdp_r = pomdp_r
self._level_shape = level_shape
if self._pomdp_r:
self._array = np.zeros((1, pomdp_r * 2 + 1, pomdp_r * 2 + 1))
else:
self._array = np.zeros((1, *self._level_shape))
def __init__(self, initial_charge_level: float, *args, **kwargs):
super(Battery, self).__init__(*args, **kwargs)
self.charge_level = initial_charge_level
def as_array(self):
self._array[:] = c.FREE_CELL.value
self._array[0, 0] = self.charge_level
return self._array
def __repr__(self):
return f'{self.__class__.__name__}[{self.agent.name}]({self.charge_level})'
def encoding(self):
return self.charge_level
def charge(self, amount) -> c:
if self.charge_level < 1:
@ -73,12 +49,10 @@ class Battery(object):
if self.charge_level != 0:
# noinspection PyTypeChecker
self.charge_level = max(0, amount + self.charge_level)
self._register.notify_change_to_value(self)
return c.VALID
else:
return c.NOT_VALID
def belongs_to_entity(self, entity):
return self.agent == entity
return c.NOT_VALID
def summarize_state(self, **kwargs):
attr_dict = {key: str(val) for key, val in self.__dict__.items() if not key.startswith('_') and key != 'data'}
@ -86,7 +60,7 @@ class Battery(object):
return attr_dict
class BatteriesRegister(ObjectRegister):
class BatteriesRegister(EnvObjectRegister):
_accepted_objects = Battery
is_blocking_light = False
@ -98,16 +72,17 @@ class BatteriesRegister(ObjectRegister):
self.is_observable = True
def as_array(self):
# self._array[:] = c.FREE_CELL.value
# ToDO: Make this Lazy
self._array[:] = c.FREE_CELL.value
for inv_idx, battery in enumerate(self):
self._array[inv_idx] = battery.as_array()
return self._array
def spawn_batteries(self, agents, pomdp_r, initial_charge_level):
inventories = [self._accepted_objects(pomdp_r, self._level_shape, agent,
initial_charge_level)
for _, agent in enumerate(agents)]
self.register_additional_items(inventories)
batteries = [self._accepted_objects(pomdp_r, self._shape, agent,
initial_charge_level)
for _, agent in enumerate(agents)]
self.register_additional_items(batteries)
def idx_by_entity(self, entity):
try:
@ -135,7 +110,7 @@ class ChargePod(Entity):
@property
def encoding(self):
return ITEM_DROP_OFF
return CHARGE_POD
def __init__(self, *args, charge_rate: float = 0.4,
multi_charge: bool = False, **kwargs):
@ -157,11 +132,12 @@ class ChargePod(Entity):
return summary
class ChargePods(EntityObjectRegister):
class ChargePods(EntityRegister):
_accepted_objects = ChargePod
def as_array(self):
@DeprecationWarning
def Xas_array(self):
self._array[:] = c.FREE_CELL.value
for item in self:
if item.pos != c.NO_POS.value:
@ -180,6 +156,16 @@ class BatteryFactory(BaseFactory):
self.btry_prop = btry_prop
super().__init__(*args, **kwargs)
def _additional_raw_observations(self, agent) -> Dict[Constants, np.typing.ArrayLike]:
additional_raw_observations = super()._additional_raw_observations(agent)
additional_raw_observations.update({c.BATTERIES: self[c.BATTERIES].by_entity(agent).as_array()})
return additional_raw_observations
def _additional_observations(self) -> Dict[Constants, np.typing.ArrayLike]:
additional_observations = super()._additional_observations()
additional_observations.update({c.CHARGE_POD: self[c.CHARGE_POD].as_array()})
return additional_observations
@property
def additional_entities(self):
super_entities = super().additional_entities

58
environments/factory/factory_destination.py → environments/factory/factory_dest.py
View File

@ -6,10 +6,10 @@ import numpy as np
import random
from environments.factory.base.base_factory import BaseFactory
from environments.helpers import Constants as c
from environments.helpers import Constants as c, Constants
from environments import helpers as h
from environments.factory.base.objects import Agent, Entity, Action, Tile
from environments.factory.base.registers import Entities, MovingEntityObjectRegister
from environments.factory.base.objects import Agent, Entity, Action
from environments.factory.base.registers import Entities, EntityRegister
from environments.factory.base.renderer import RenderEntity
@ -62,13 +62,16 @@ class Destination(Entity):
return state_summary
class Destinations(MovingEntityObjectRegister):
class Destinations(EntityRegister):
_accepted_objects = Destination
_light_blocking = False
def as_array(self):
self._array[:] = c.FREE_CELL.value
# ToDo: Switch to new Style Array Put
# indices = list(zip(range(len(self)), *zip(*[x.pos for x in self])))
# np.put(self._array, [np.ravel_multi_index(x, self._array.shape) for x in indices], self.encodings)
for item in self:
if item.pos != c.NO_POS.value:
self._array[0, item.x, item.y] = item.encoding
@ -83,39 +86,39 @@ class ReachedDestinations(Destinations):
_light_blocking = False
def __init__(self, *args, **kwargs):
super(ReachedDestinations, self).__init__(*args, is_observable=False, **kwargs)
super(ReachedDestinations, self).__init__(*args, **kwargs)
def summarize_states(self, n_steps=None):
return {}
class DestSpawnMode(object):
class DestModeOptions(object):
DONE = 'DONE'
GROUPED = 'GROUPED'
PER_DEST = 'PER_DEST'
class DestinationProperties(NamedTuple):
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 = DestSpawnMode.DONE
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 == DestSpawnMode.DONE) != bool(spawn_frequency)
assert (spawn_mode == DestModeOptions.DONE) != bool(spawn_frequency)
# noinspection PyAttributeOutsideInit, PyAbstractClass
class DestinationFactory(BaseFactory):
class DestFactory(BaseFactory):
# noinspection PyMissingConstructor
def __init__(self, *args, dest_prop: DestinationProperties = DestinationProperties(),
def __init__(self, *args, dest_prop: DestProperties = DestProperties(),
env_seed=time.time_ns(), **kwargs):
if isinstance(dest_prop, dict):
dest_prop = DestinationProperties(**dest_prop)
dest_prop = DestProperties(**dest_prop)
self.dest_prop = dest_prop
kwargs.update(env_seed=env_seed)
self._dest_rng = np.random.default_rng(env_seed)
@ -145,10 +148,6 @@ class DestinationFactory(BaseFactory):
super_entities.update({c.DESTINATION: destinations, c.REACHEDDESTINATION: reached_destinations})
return super_entities
def additional_per_agent_obs_build(self, agent) -> List[np.ndarray]:
additional_per_agent_obs_build = super().additional_per_agent_obs_build(agent)
return additional_per_agent_obs_build
def wait(self, agent: Agent):
if destiantion := self[c.DESTINATION].by_pos(agent.pos):
valid = destiantion.wait(agent)
@ -178,13 +177,13 @@ class DestinationFactory(BaseFactory):
if val == self.dest_prop.spawn_frequency]
if destinations_to_spawn:
n_dest_to_spawn = len(destinations_to_spawn)
if self.dest_prop.spawn_mode != DestSpawnMode.GROUPED:
if self.dest_prop.spawn_mode != DestModeOptions.GROUPED:
destinations = [Destination(tile) for tile in self[c.FLOOR].empty_tiles[:n_dest_to_spawn]]
self[c.DESTINATION].register_additional_items(destinations)
for dest in destinations_to_spawn:
del self._dest_spawn_timer[dest]
self.print(f'{n_dest_to_spawn} new destinations have been spawned')
elif self.dest_prop.spawn_mode == DestSpawnMode.GROUPED and n_dest_to_spawn == self.dest_prop.n_dests:
elif self.dest_prop.spawn_mode == DestModeOptions.GROUPED and n_dest_to_spawn == self.dest_prop.n_dests:
destinations = [Destination(tile) for tile in self[c.FLOOR].empty_tiles[:n_dest_to_spawn]]
self[c.DESTINATION].register_additional_items(destinations)
for dest in destinations_to_spawn:
@ -204,7 +203,7 @@ class DestinationFactory(BaseFactory):
for dest in list(self[c.DESTINATION].values()):
if dest.is_considered_reached:
self[c.REACHEDDESTINATION].register_item(dest)
self[c.DESTINATION].delete_entity(dest)
self[c.DESTINATION].delete_env_object(dest)
self._dest_spawn_timer[dest.name] = 0
self.print(f'{dest.name} is reached now, removing...')
else:
@ -219,6 +218,11 @@ class DestinationFactory(BaseFactory):
self.trigger_destination_spawn()
return info_dict
def _additional_observations(self) -> Dict[Constants, np.typing.ArrayLike]:
additional_observations = super()._additional_observations()
additional_observations.update({c.DESTINATION: self[c.DESTINATION].as_array()})
return additional_observations
def calculate_additional_reward(self, agent: Agent) -> (int, dict):
# noinspection PyUnresolvedReferences
reward, info_dict = super().calculate_additional_reward(agent)
@ -240,7 +244,7 @@ class DestinationFactory(BaseFactory):
info_dict.update(agent_reached_destination=1)
self.print(f'{agent.name} just reached destination at {agent.pos}')
reward += 0.5
self[c.REACHEDDESTINATION].delete_entity(reached_dest)
self[c.REACHEDDESTINATION].delete_env_object(reached_dest)
return reward, info_dict
def render_additional_assets(self, mode='human'):
@ -256,7 +260,7 @@ if __name__ == '__main__':
render = True
dest_probs = DestinationProperties(n_dests=2, spawn_frequency=5, spawn_mode=DestSpawnMode.GROUPED)
dest_probs = DestProperties(n_dests=2, spawn_frequency=5, spawn_mode=DestModeOptions.GROUPED)
obs_props = ObservationProperties(render_agents=ARO.LEVEL, omit_agent_self=True, pomdp_r=2)
@ -264,12 +268,12 @@ if __name__ == '__main__':
'allow_diagonal_movement': False,
'allow_no_op': False}
factory = DestinationFactory(n_agents=10, done_at_collision=False,
level_name='rooms', max_steps=400,
obs_prop=obs_props, parse_doors=True,
verbose=True,
mv_prop=move_props, dest_prop=dest_probs
)
factory = DestFactory(n_agents=10, done_at_collision=False,
level_name='rooms', max_steps=400,
obs_prop=obs_props, parse_doors=True,
verbose=True,
mv_prop=move_props, dest_prop=dest_probs
)
# noinspection DuplicatedCode
n_actions = factory.action_space.n - 1

97
environments/factory/factory_dirt.py
View File

@ -6,11 +6,11 @@ import random
import numpy as np
from algorithms.TSP_dirt_agent import TSPDirtAgent
from environments.helpers import Constants as c
from environments.helpers import Constants as c, Constants
from environments import helpers as h
from environments.factory.base.base_factory import BaseFactory
from environments.factory.base.objects import Agent, Action, Entity, Tile
from environments.factory.base.registers import Entities, MovingEntityObjectRegister
from environments.factory.base.registers import Entities, MovingEntityObjectRegister, EntityRegister
from environments.factory.base.renderer import RenderEntity
from environments.utility_classes import ObservationProperties
@ -42,6 +42,7 @@ class Dirt(Entity):
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
@ -52,6 +53,7 @@ class Dirt(Entity):
def set_new_amount(self, amount):
self._amount = amount
self._register.notify_change_to_value(self)
def summarize_state(self, **kwargs):
state_dict = super().summarize_state(**kwargs)
@ -59,18 +61,7 @@ class Dirt(Entity):
return state_dict
class DirtRegister(MovingEntityObjectRegister):
def as_array(self):
if self._array is not None:
self._array[:] = c.FREE_CELL.value
for dirt in list(self.values()):
if dirt.amount == 0:
self.delete_entity(dirt)
self._array[0, dirt.x, dirt.y] = dirt.amount
else:
self._array = np.zeros((1, *self._level_shape))
return self._array
class DirtRegister(EntityRegister):
_accepted_objects = Dirt
@ -93,7 +84,7 @@ class DirtRegister(MovingEntityObjectRegister):
if not self.amount > self.dirt_properties.max_global_amount:
dirt = self.by_pos(tile.pos)
if dirt is None:
dirt = Dirt(tile, amount=self.dirt_properties.max_spawn_amount)
dirt = Dirt(tile, self, amount=self.dirt_properties.max_spawn_amount)
self.register_item(dirt)
else:
new_value = dirt.amount + self.dirt_properties.max_spawn_amount
@ -155,7 +146,7 @@ class DirtFactory(BaseFactory):
new_dirt_amount = dirt.amount - self.dirt_prop.clean_amount
if new_dirt_amount <= 0:
self[c.DIRT].delete_entity(dirt)
self[c.DIRT].delete_env_object(dirt)
else:
dirt.set_new_amount(max(new_dirt_amount, c.FREE_CELL.value))
return c.VALID
@ -224,6 +215,11 @@ class DirtFactory(BaseFactory):
done = self.dirt_prop.done_when_clean and (len(self[c.DIRT]) == 0)
return super_done or done
def _additional_observations(self) -> Dict[Constants, np.typing.ArrayLike]:
additional_observations = super()._additional_observations()
additional_observations.update({c.DIRT: self[c.DIRT].as_array()})
return additional_observations
def calculate_additional_reward(self, agent: Agent) -> (int, dict):
reward, info_dict = super().calculate_additional_reward(agent)
dirt = [dirt.amount for dirt in self[c.DIRT]]
@ -278,41 +274,52 @@ if __name__ == '__main__':
)
obs_props = ObservationProperties(render_agents=ARO.COMBINED, omit_agent_self=True,
pomdp_r=2, additional_agent_placeholder=None)
pomdp_r=2, additional_agent_placeholder=None, cast_shadows=True)
move_props = {'allow_square_movement': True,
'allow_diagonal_movement': False,
'allow_no_op': False}
global_timings = []
for i in range(20):
factory = DirtFactory(n_agents=1, done_at_collision=False,
level_name='rooms', max_steps=400,
doors_have_area=False,
obs_prop=obs_props, parse_doors=True,
record_episodes=True, verbose=True,
mv_prop=move_props, dirt_prop=dirt_props,
inject_agents=[TSPDirtAgent]
)
factory = DirtFactory(n_agents=2, done_at_collision=False,
level_name='rooms', max_steps=1000,
doors_have_area=False,
obs_prop=obs_props, parse_doors=True,
record_episodes=True, verbose=True,
mv_prop=move_props, dirt_prop=dirt_props,
# inject_agents=[TSPDirtAgent],
)
# noinspection DuplicatedCode
n_actions = factory.action_space.n - 1
_ = factory.observation_space
for epoch in range(10):
random_actions = [[random.randint(0, n_actions) for _
in range(factory.n_agents)] for _
in range(factory.max_steps+1)]
env_state = factory.reset()
if render:
factory.render()
tsp_agent = factory.get_injected_agents()[0]
r = 0
for agent_i_action in random_actions:
env_state, step_r, done_bool, info_obj = factory.step(tsp_agent.predict())
r += step_r
# noinspection DuplicatedCode
n_actions = factory.action_space.n - 1
_ = factory.observation_space
obs_space = factory.observation_space
obs_space_named = factory.named_observation_space
times = []
import time
for epoch in range(10):
start_time = time.time()
random_actions = [[random.randint(0, n_actions) for _
in range(factory.n_agents)] for _
in range(factory.max_steps+1)]
env_state = factory.reset()
if render:
factory.render()
if done_bool:
break
print(f'Factory run {epoch} done, reward is:\n {r}')
# tsp_agent = factory.get_injected_agents()[0]
r = 0
for agent_i_action in random_actions:
env_state, step_r, done_bool, info_obj = factory.step(agent_i_action)
r += step_r
if render:
factory.render()
if done_bool:
break
times.append(time.time() - start_time)
# print(f'Factory run {epoch} done, reward is:\n {r}')
print('Time Taken: ', sum(times) / 10)
global_timings.append(sum(times) / 10)
print('Time Taken: ', sum(global_timings[10:]) / 10)
pass

108
environments/factory/factory_item.py
View File

@ -6,11 +6,11 @@ import numpy as np
import random
from environments.factory.base.base_factory import BaseFactory
from environments.helpers import Constants as c
from environments.helpers import Constants as c, Constants
from environments import helpers as h
from environments.factory.base.objects import Agent, Entity, Action, Tile, MoveableEntity
from environments.factory.base.registers import Entities, EntityObjectRegister, ObjectRegister, \
MovingEntityObjectRegister
from environments.factory.base.registers import Entities, EntityRegister, EnvObjectRegister, MovingEntityObjectRegister, \
BoundRegisterMixin
from environments.factory.base.renderer import RenderEntity
@ -19,7 +19,7 @@ NO_ITEM = 0
ITEM_DROP_OFF = 1
class Item(MoveableEntity):
class Item(Entity):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
@ -41,20 +41,21 @@ class Item(MoveableEntity):
def set_auto_despawn(self, auto_despawn):
self._auto_despawn = auto_despawn
def despawn(self):
# Todo: Move this to base class?
curr_tile = self.tile
curr_tile.leave(self)
self._tile = None
self._register.notify_change_to_value(self)
return True
class ItemRegister(MovingEntityObjectRegister):
def as_array(self):
self._array[:] = c.FREE_CELL.value
for item in self:
if item.pos != c.NO_POS.value:
self._array[0, item.x, item.y] = item.encoding
return self._array
class ItemRegister(EntityRegister):
_accepted_objects = Item
def spawn_items(self, tiles: List[Tile]):
items = [Item(tile) for tile in tiles]
items = [Item(tile, self) for tile in tiles]
self.register_additional_items(items)
def despawn_items(self, items: List[Item]):
@ -63,7 +64,7 @@ class ItemRegister(MovingEntityObjectRegister):
del self[item]
class Inventory(UserList):
class Inventory(EntityRegister, BoundRegisterMixin):
@property
def is_blocking_light(self):
@ -73,19 +74,18 @@ class Inventory(UserList):
def name(self):
return f'{self.__class__.__name__}({self.agent.name})'
def __init__(self, pomdp_r: int, level_shape: (int, int), agent: Agent, capacity: int):
def __init__(self, obs_shape: (int, int), agent: Agent, capacity: int):
super(Inventory, self).__init__()
self.agent = agent
self.pomdp_r = pomdp_r
self._level_shape = level_shape
if self.pomdp_r:
self._array = np.zeros((1, pomdp_r * 2 + 1, pomdp_r * 2 + 1))
else:
self._array = np.zeros((1, *self._level_shape))
self._obs_shape = obs_shape
self._array = np.zeros((1, *self._obs_shape))
self.capacity = min(capacity, self._array.size)
def as_array(self):
self._array[:] = c.FREE_CELL.value
# ToDo: Make this Lazy
for item_idx, item in enumerate(self):
x_diff, y_diff = divmod(item_idx, self._array.shape[1])
self._array[0, int(x_diff), int(y_diff)] = item.encoding
@ -110,25 +110,22 @@ class Inventory(UserList):
return attr_dict
class Inventories(ObjectRegister):
class Inventories(EnvObjectRegister):
_accepted_objects = Inventory
is_blocking_light = False
can_be_shadowed = False
hide_from_obs_builder = True
def __init__(self, *args, **kwargs):
def __init__(self, obs_shape, *args, **kwargs):
super(Inventories, self).__init__(*args, is_per_agent=True, individual_slices=True, **kwargs)
self.is_observable = True
self._obs_shape = obs_shape
def as_array(self):
# self._array[:] = c.FREE_CELL.value
for inv_idx, inventory in enumerate(self):
self._array[inv_idx] = inventory.as_array()
return self._array
return np.stack([inventory.as_array() for inv_idx, inventory in enumerate(self)])
def spawn_inventories(self, agents, pomdp_r, capacity):
inventories = [self._accepted_objects(pomdp_r, self._level_shape, agent, capacity)
def spawn_inventories(self, agents, capacity):
inventories = [self._accepted_objects(self._obs_shape, agent, capacity)
for _, agent in enumerate(agents)]
self.register_additional_items(inventories)
@ -183,20 +180,20 @@ class DropOffLocation(Entity):
return super().summarize_state(n_steps=n_steps)
class DropOffLocations(EntityObjectRegister):
class DropOffLocations(EntityRegister):
_accepted_objects = DropOffLocation
def as_array(self):
@DeprecationWarning
def Xas_array(self):
# Todo: Which is faster?
# indices = list(zip(range(len(self)), *zip(*[x.pos for x in self])))
# np.put(self._array, [np.ravel_multi_index(x, self._array.shape) for x in indices], self.encodings)
self._array[:] = c.FREE_CELL.value
for item in self:
if item.pos != c.NO_POS.value:
self._array[0, item.x, item.y] = item.encoding
indices = list(zip([0, ] * len(self), *zip(*[x.pos for x in self])))
np.put(self._array, [np.ravel_multi_index(x, self._array.shape) for x in indices], self.encodings)
return self._array
def __repr__(self):
super(DropOffLocations, self).__repr__()
class ItemProperties(NamedTuple):
n_items: int = 5 # How many items are there at the same time
@ -241,17 +238,23 @@ class ItemFactory(BaseFactory):
empty_tiles = self[c.FLOOR].empty_tiles[:self.item_prop.n_items]
item_register.spawn_items(empty_tiles)
inventories = Inventories(self._level_shape if not self._pomdp_r else ((self.pomdp_diameter,) * 2))
inventories.spawn_inventories(self[c.AGENT], self._pomdp_r,
self.item_prop.max_agent_inventory_capacity)
inventories = Inventories(self._level_shape if not self._pomdp_r else ((self.pomdp_diameter,) * 2),
self._level_shape)
inventories.spawn_inventories(self[c.AGENT], self.item_prop.max_agent_inventory_capacity)
super_entities.update({c.DROP_OFF: drop_offs, c.ITEM: item_register, c.INVENTORY: inventories})
return super_entities
def additional_per_agent_obs_build(self, agent) -> List[np.ndarray]:
additional_per_agent_obs_build = super().additional_per_agent_obs_build(agent)
additional_per_agent_obs_build.append(self[c.INVENTORY].by_entity(agent).as_array())
return additional_per_agent_obs_build
def _additional_raw_observations(self, agent) -> Dict[Constants, np.typing.ArrayLike]:
additional_raw_observations = super()._additional_raw_observations(agent)
additional_raw_observations.update({c.INVENTORY: self[c.INVENTORY].by_entity(agent).as_array()})
return additional_raw_observations
def _additional_observations(self) -> Dict[Constants, np.typing.ArrayLike]:
additional_observations = super()._additional_observations()
additional_observations.update({c.ITEM: self[c.ITEM].as_array()})
additional_observations.update({c.DROP_OFF: self[c.DROP_OFF].as_array()})
return additional_observations
def do_item_action(self, agent: Agent):
inventory = self[c.INVENTORY].by_entity(agent)
@ -264,7 +267,7 @@ class ItemFactory(BaseFactory):
elif item := self[c.ITEM].by_pos(agent.pos):
try:
inventory.append(item)
item.move(self._NO_POS_TILE)
item.despawn()
return c.VALID
except RuntimeError:
return c.NOT_VALID
@ -308,7 +311,7 @@ class ItemFactory(BaseFactory):
if item.auto_despawn >= 1:
item.set_auto_despawn(item.auto_despawn-1)
elif not item.auto_despawn:
self[c.ITEM].delete_entity(item)
self[c.ITEM].delete_env_object(item)
else:
pass
@ -327,12 +330,12 @@ class ItemFactory(BaseFactory):
info_dict.update({f'{agent.name}_item_drop_off': 1})
info_dict.update(item_drop_off=1)
self.print(f'{agent.name} just dropped of an item at {drop_off.pos}.')
reward += 0.5
reward += 1
else:
info_dict.update({f'{agent.name}_item_pickup': 1})
info_dict.update(item_pickup=1)
self.print(f'{agent.name} just picked up an item at {agent.pos}')
reward += 0.1
reward += 0.2
else:
if self[c.DROP_OFF].by_pos(agent.pos):
info_dict.update({f'{agent.name}_failed_drop_off': 1})
@ -363,13 +366,13 @@ if __name__ == '__main__':
item_probs = ItemProperties()
obs_props = ObservationProperties(render_agents=ARO.LEVEL, omit_agent_self=True, pomdp_r=2)
obs_props = ObservationProperties(render_agents=ARO.SEPERATE, omit_agent_self=True, pomdp_r=2)
move_props = {'allow_square_movement': True,
'allow_diagonal_movement': False,
'allow_diagonal_movement': True,
'allow_no_op': False}
factory = ItemFactory(n_agents=3, done_at_collision=False,
factory = ItemFactory(n_agents=2, done_at_collision=False,
level_name='rooms', max_steps=400,
obs_prop=obs_props, parse_doors=True,
record_episodes=True, verbose=True,
@ -378,7 +381,8 @@ if __name__ == '__main__':
# noinspection DuplicatedCode
n_actions = factory.action_space.n - 1
_ = factory.observation_space
obs_space = factory.observation_space
obs_space_named = factory.named_observation_space
for epoch in range(4):
random_actions = [[random.randint(0, n_actions) for _

64
environments/helpers.py
View File

@ -1,12 +1,12 @@
import itertools
from collections import defaultdict
from enum import Enum, auto
from typing import Tuple, Union
from enum import Enum
from pathlib import Path
from typing import Tuple, Union, Dict, List
import networkx as nx
import numpy as np
from pathlib import Path
from numpy.typing import ArrayLike
from stable_baselines3 import PPO, DQN, A2C
MODEL_MAP = dict(PPO=PPO, DQN=DQN, A2C=A2C)
@ -29,6 +29,7 @@ class Constants(Enum):
LEVEL = 'Level'
AGENT = 'Agent'
AGENT_PLACEHOLDER = 'AGENT_PLACEHOLDER'
GLOBAL_POSITION = 'GLOBAL_POSITION'
FREE_CELL = 0
OCCUPIED_CELL = 1
SHADOWED_CELL = -1
@ -109,6 +110,58 @@ ACTIONMAP = defaultdict(lambda: (0, 0), {m.NORTH: (-1, 0), m.NORTHEAST: (-1, +1)
)
class ObservationTranslator:
def __init__(self, obs_shape_2d: (int, int), this_named_observation_space: Dict[str, dict],
*per_agent_named_obs_space: Dict[str, dict],
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)
elif placeholder_fill_value.lower() in ['uniform', 'u']:
self.random_fill = lambda: np.random.uniform(size=self.obs_shape)
else:
raise ValueError('Please chooe between "uniform" or "normal"')
else:
self.random_fill = None
self._this_named_obs_space = this_named_observation_space
self._per_agent_named_obs_space = list(per_agent_named_obs_space)
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['explained_idxs'] for name, idx_space_dict in target_obs_space.items()]
flat_translation = [x for y in translation for x in y]
return np.take(obs, flat_translation, axis=1 if obs.ndim == 4 else 0)
def translate_observations(self, observations: List[ArrayLike]):
return [self.translate_observation(idx, observation) for idx, observation in enumerate(observations)]
def __call__(self, observations):
return self.translate_observations(observations)
class ActionTranslator:
def __init__(self, target_named_action_space: Dict[str, int], *per_agent_named_action_space: Dict[str, int]):
self._target_named_action_space = target_named_action_space
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]
def translate_action(self, agent_idx: int, action: int):
named_action = self._per_agent_idx_actions[agent_idx][action]
translated_action = self._target_named_action_space[named_action]
return translated_action
def translate_actions(self, actions: List[int]):
return [self.translate_action(idx, action) for idx, action in enumerate(actions)]
def __call__(self, actions):
return self.translate_actions(actions)
# Utility functions
def parse_level(path):
with path.open('r') as lvl:
@ -128,7 +181,7 @@ def one_hot_level(level, wall_char: Union[c, str] = c.WALL):
return binary_grid
def check_position(slice_to_check_against: np.ndarray, position_to_check: Tuple[int, int]):
def check_position(slice_to_check_against: ArrayLike, position_to_check: Tuple[int, int]):
x_pos, y_pos = position_to_check
# Check if agent colides with grid boundrys
@ -177,6 +230,7 @@ def points_to_graph(coordiniates_or_tiles, allow_euclidean_connections=True, all
graph.add_edge(a, b)
return graph
if __name__ == '__main__':
parsed_level = parse_level(Path(__file__).parent / 'factory' / 'levels' / 'simple.txt')
y = one_hot_level(parsed_level)

4
environments/utility_classes.py
View File

@ -20,10 +20,10 @@ class ObservationProperties(NamedTuple):
render_agents: AgentRenderOptions = AgentRenderOptions.SEPERATE
omit_agent_self: bool = True
additional_agent_placeholder: Union[None, str, int] = None
cast_shadows = True
cast_shadows: bool = True
frames_to_stack: int = 0
pomdp_r: int = 0
show_global_position_info: bool = True
show_global_position_info: bool = False
class MarlFrameStack(gym.ObservationWrapper):

15
reload_agent.py
View File

@ -3,6 +3,7 @@ from pathlib import Path
import numpy as np
import yaml
from stable_baselines3 import A2C
from environments import helpers as h
from environments.helpers import Constants as c
@ -16,13 +17,12 @@ warnings.filterwarnings('ignore', category=UserWarning)
if __name__ == '__main__':
determin = False
determin = True
render = True
record = True
record = False
seed = 67
n_agents = 1
out_path = Path('study_out/e_1_new_reward/no_obs/dirt/A2C_new_reward/0_A2C_new_reward')
out_path_2 = Path('study_out/e_1_obs_stack_3_gae_0.25_n_steps_16/seperate_N/dirt/A2C_obs_stack_3_gae_0.25_n_steps_16/1_A2C_obs_stack_3_gae_0.25_n_steps_16')
out_path = Path('study_out/single_run_with_export/dirt')
model_path = out_path
with (out_path / f'env_params.json').open('r') as f:
@ -35,10 +35,9 @@ if __name__ == '__main__':
env_kwargs.update(record_episodes=record, done_at_collision=True)
this_model = out_path / 'model.zip'
other_model = out_path / 'model.zip'
model_cls = next(val for key, val in h.MODEL_MAP.items() if key in out_path.parent.name)
models = [model_cls.load(this_model)] # , model_cls.load(other_model)]
model_cls = A2C # next(val for key, val in h.MODEL_MAP.items() if key in out_path.parent.name)
models = [model_cls.load(this_model)]
# Init Env
with DirtFactory(**env_kwargs) as env:
@ -59,6 +58,8 @@ if __name__ == '__main__':
rew += step_r
if render:
env.render()
if not env.unwrapped.unwrapped[c.AGENT][0].temp_valid:
print('Invalid ACtions')
if done_bool:
break
print(f'Factory run {episode} done, reward is:\n {rew}')

10
studies/e_1.py
View File

@ -434,8 +434,8 @@ if __name__ == '__main__':
# Iteration
start_mp_baseline_run(env_map, policy_path)
# for seed_path in (y for y in policy_path.iterdir() if y.is_dir()):
# load_model_run_baseline(seed_path)
# for policy_path in (y for y in policy_path.iterdir() if y.is_dir()):
# load_model_run_baseline(policy_path)
print('Baseline Tracking done')
# Then iterate over every model and monitor "ood behavior" - "is it ood?"
@ -448,11 +448,11 @@ if __name__ == '__main__':
for policy_path in [x for x in env_path.iterdir() if x. is_dir()]:
# FIXME: Pick random seed or iterate over available seeds
# First seed path version
# seed_path = next((y for y in policy_path.iterdir() if y.is_dir()))
# policy_path = next((y for y in policy_path.iterdir() if y.is_dir()))
# Iteration
start_mp_study_run(env_map, policy_path)
#for seed_path in (y for y in policy_path.iterdir() if y.is_dir()):
# load_model_run_study(seed_path, env_map[env_path.name][0], observation_modes[obs_mode])
#for policy_path in (y for y in policy_path.iterdir() if y.is_dir()):
# load_model_run_study(policy_path, env_map[env_path.name][0], observation_modes[obs_mode])
print('OOD Tracking Done')
# Plotting

226
studies/single_run_with_export.py Normal file
View File

@ -0,0 +1,226 @@
import sys
from pathlib import Path
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.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_item import ItemProperties, ItemFactory
from environments.factory.factory_dest import DestProperties, DestFactory, DestModeOptions
from environments.factory.combined_factories import DirtDestItemFactory
from environments.logging.envmonitor import EnvMonitor
from environments.utility_classes import MovementProperties, ObservationProperties, AgentRenderOptions
"""
In this studie, we want to export trained Agents for debugging purposes.
"""
def encapsule_env_factory(env_fctry, env_kwrgs):
def _init():
with env_fctry(**env_kwrgs) as init_env:
return init_env
return _init
def load_model_run_baseline(policy_path, env_to_run):
# retrieve model class
model_cls = h.MODEL_MAP['A2C']
# Load both agents
model = model_cls.load(policy_path / 'model.zip', device='cpu')
# Load old env kwargs
with next(policy_path.glob('*.json')).open('r') as f:
env_kwargs = simplejson.load(f)
env_kwargs.update(done_at_collision=True)
# Init Env
with env_to_run(**env_kwargs) as env_factory:
monitored_env_factory = EnvMonitor(env_factory)
recorded_env_factory = EnvRecorder(monitored_env_factory)
# Evaluation Loop for i in range(n Episodes)
for episode in range(5):
env_state = recorded_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 = recorded_env_factory.step(action)
rew += step_r
if done_bool:
break
print(f'Factory run {episode} done, reward is:\n {rew}')
recorded_env_factory.save_run(filepath=policy_path / f'monitor.pick')
recorded_env_factory.save_records(filepath=policy_path / f'recorder.json')
def load_model_run_combined(root_path, env_to_run, env_kwargs):
# retrieve model class
model_cls = h.MODEL_MAP['A2C']
# Load both agents
models = [model_cls.load(model_zip, device='cpu') for model_zip in root_path.rglob('model.zip')]
# Load old env kwargs
env_kwargs = env_kwargs.copy()
env_kwargs.update(
n_agents=len(models),
done_at_collision=False)
# Init Env
with env_to_run(**env_kwargs) as env_factory:
action_translator = ActionTranslator(env_factory.named_action_space,
*[x.named_action_space for x in models])
observation_translator = ObservationTranslator(env_factory.observation_space.shape[-2:],
env_factory.named_observation_space,
*[x.named_observation_space for x in models])
monitored_env_factory = EnvMonitor(env_factory)
recorded_env_factory = EnvRecorder(monitored_env_factory)
# Evaluation Loop for i in range(n Episodes)
for episode in range(5):
env_state = recorded_env_factory.reset()
rew, done_bool = 0, False
while not done_bool:
translated_observations = observation_translator(env_state)
actions = [model.predict(translated_observations[model_idx], deterministic=True)[0]
for model_idx, model in enumerate(models)]
translated_actions = action_translator(actions)
env_state, step_r, done_bool, info_obj = recorded_env_factory.step(translated_actions)
rew += step_r
if done_bool:
break
print(f'Factory run {episode} done, reward is:\n {rew}')
recorded_env_factory.save_run(filepath=policy_path / f'monitor.pick')
recorded_env_factory.save_records(filepath=policy_path / f'recorder.json')
if __name__ == '__main__':
# What to do:
train = True
individual_run = True
combined_run = True
train_steps = 2e6
frames_to_stack = 3
# Define a global studi save path
study_root_path = Path(__file__).parent.parent / 'study_out' / f'{Path(__file__).stem}'
# Define Global Env Parameters
# Define properties object parameters
obs_props = ObservationProperties(render_agents=AgentRenderOptions.NOT,
additional_agent_placeholder=None,
omit_agent_self=True,
frames_to_stack=frames_to_stack,
pomdp_r=2, cast_shadows=True)
move_props = MovementProperties(allow_diagonal_movement=True,
allow_square_movement=True,
allow_no_op=False)
dirt_props = DirtProperties(initial_dirt_ratio=0.35, initial_dirt_spawn_r_var=0.1,
clean_amount=0.34,
max_spawn_amount=0.1, max_global_amount=20,
max_local_amount=1, spawn_frequency=0, max_spawn_ratio=0.05,
dirt_smear_amount=0.0, agent_can_interact=True)
item_props = ItemProperties(n_items=10, agent_can_interact=True,
spawn_frequency=30, n_drop_off_locations=2,
max_agent_inventory_capacity=15)
dest_props = DestProperties(n_dests=4, spawn_mode=DestModeOptions.GROUPED, spawn_frequency=1)
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,
obs_prop=obs_props,
done_at_collision=False
)
# Bundle both environments with global kwargs and parameters
env_map = {}
env_map.update({'dirt': (DirtFactory, dict(dirt_prop=dirt_props,
**factory_kwargs.copy()))})
env_map.update({'item': (ItemFactory, dict(item_prop=item_props,
**factory_kwargs.copy()))})
env_map.update({'dest': (DestFactory, dict(dest_prop=dest_props,
**factory_kwargs.copy()))})
env_map.update({'combined': (DirtDestItemFactory, dict(dest_prop=dest_props,
item_prop=item_props,
dirt_prop=dirt_props,
**factory_kwargs.copy()))})
env_names = list(env_map.keys())
# Train starts here ############################################################
# Build Major Loop parameters, parameter versions, Env Classes and models
if train:
for env_key in (env_key for env_key in env_map if 'combined' != env_key):
model_cls = h.MODEL_MAP['A2C']
combination_path = study_root_path / env_key
env_class, env_kwargs = env_map[env_key]
# Output folder
if (combination_path / 'monitor.pick').exists():
continue
combination_path.mkdir(parents=True, exist_ok=True)
with env_class(**env_kwargs) as env_factory:
param_path = combination_path / f'env_params.json'
env_factory.save_params(param_path)
# EnvMonitor Init
callbacks = [EnvMonitor(env_factory)]
# Model Init
model = model_cls("MlpPolicy", env_factory,
verbose=1, seed=69, device='cpu')
# Model train
model.learn(total_timesteps=int(train_steps), callback=callbacks)
# Model save
model.named_action_space = env_factory.unwrapped.named_action_space
model.named_observation_space = env_factory.unwrapped.named_observation_space
save_path = combination_path / f'model.zip'
model.save(save_path)
# Monitor Save
callbacks[0].save_run(combination_path / 'monitor.pick')
# Better be save then sorry: Clean up!
del env_factory, model
import gc
gc.collect()
# Train ends here ############################################################
# Evaluation starts here #####################################################
# First Iterate over every model and monitor "as trained"
if individual_run:
print('Start Individual Recording')
for env_key in (env_key for env_key in env_map if 'combined' != env_key):
# For trained policy in study_root_path / identifier
policy_path = study_root_path / env_key
load_model_run_baseline(policy_path, env_map[policy_path.name][0])
# for policy_path in (y for y in policy_path.iterdir() if y.is_dir()):
# load_model_run_baseline(policy_path)
print('Start Individual Training')
# Then iterate over every model and monitor "ood behavior" - "is it ood?"
if combined_run:
print('Start combined run')
for env_key in (env_key for env_key in env_map if 'combined' == env_key):
# For trained policy in study_root_path / identifier
factory, kwargs = env_map[env_key]
load_model_run_combined(study_root_path, factory, kwargs)
print('OOD Tracking Done')