import time
from pathlib import Path
from typing import List, Union, NamedTuple, Dict
import random
import numpy as np
from algorithms.TSP_dirt_agent import TSPDirtAgent
from environments.helpers import Constants as BaseConstants
from environments.helpers import EnvActions as BaseActions
from environments.factory.base.base_factory import BaseFactory
from environments.factory.base.objects import Agent, Action, Entity, Floor
from environments.factory.base.registers import Entities, EntityCollection
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))
return e_x / e_x.sum()
def entropy(x):
return -(x * np.log(x + 1e-8)).sum()
c = Constants
a = Actions
# noinspection PyAttributeOutsideInit, PyAbstractClass
class DirtFactory(BaseFactory):
@property
def actions_hook(self) -> Union[Action, List[Action]]:
super_actions = super().actions_hook
super_actions.append(Action(str_ident=a.CLEAN_UP))
return super_actions
@property
def entities_hook(self) -> Dict[(str, Entities)]:
super_entities = super().entities_hook
dirt_register = DirtRegister(self.dirt_prop, self._level_shape)
super_entities.update(({c.DIRT: dirt_register}))
return super_entities
def __init__(self, *args,
dirt_prop: DirtProperties = DirtProperties(), rewards_dirt: RewardsDirt = RewardsDirt(),
env_seed=time.time_ns(), **kwargs):
if isinstance(dirt_prop, dict):
dirt_prop = DirtProperties(**dirt_prop)
if isinstance(rewards_dirt, dict):
rewards_dirt = RewardsDirt(**rewards_dirt)
self.dirt_prop = dirt_prop
self.rewards_dirt = rewards_dirt
self._dirt_rng = np.random.default_rng(env_seed)
self._dirt: DirtRegister
kwargs.update(env_seed=env_seed)
# TODO: Reset ---> document this
super().__init__(*args, **kwargs)
def render_assets_hook(self, mode='human'):
additional_assets = super().render_assets_hook()
dirt = [RenderEntity('dirt', dirt.tile.pos, min(0.15 + dirt.amount, 1.5), 'scale')
for dirt in self[c.DIRT]]
additional_assets.extend(dirt)
return additional_assets
def do_cleanup_action(self, agent: Agent) -> (dict, dict):
if dirt := self[c.DIRT].by_pos(agent.pos):
new_dirt_amount = dirt.amount - self.dirt_prop.clean_amount
if new_dirt_amount <= 0:
self[c.DIRT].delete_env_object(dirt)
else:
dirt.set_new_amount(max(new_dirt_amount, c.FREE_CELL.value))
valid = c.VALID
self.print(f'{agent.name} did just clean up some dirt at {agent.pos}.')
info_dict = {f'{agent.name}_{a.CLEAN_UP}_VALID': 1, 'cleanup_valid': 1}
reward = self.rewards_dirt.CLEAN_UP_VALID
else:
valid = c.NOT_VALID
self.print(f'{agent.name} just tried to clean up some dirt at {agent.pos}, but failed.')
info_dict = {f'{agent.name}_{a.CLEAN_UP}_FAIL': 1, 'cleanup_fail': 1}
reward = self.rewards_dirt.CLEAN_UP_FAIL
if valid and self.dirt_prop.done_when_clean and (len(self[c.DIRT]) == 0):
reward += self.rewards_dirt.CLEAN_UP_LAST_PIECE
self.print(f'{agent.name} picked up the last piece of dirt!')
info_dict = {f'{agent.name}_{a.CLEAN_UP}_LAST_PIECE': 1}
return valid, dict(value=reward, reason=a.CLEAN_UP, info=info_dict)
def trigger_dirt_spawn(self, initial_spawn=False):
dirt_rng = self._dirt_rng
free_for_dirt = [x for x in self[c.FLOOR]
if len(x.guests) == 0 or (len(x.guests) == 1 and isinstance(next(y for y in x.guests), Dirt))
]
self._dirt_rng.shuffle(free_for_dirt)
if initial_spawn:
var = self.dirt_prop.initial_dirt_spawn_r_var
new_spawn = self.dirt_prop.initial_dirt_ratio + dirt_rng.uniform(-var, var)
else:
new_spawn = dirt_rng.uniform(0, self.dirt_prop.max_spawn_ratio)
n_dirt_tiles = max(0, int(new_spawn * len(free_for_dirt)))
self[c.DIRT].spawn_dirt(free_for_dirt[:n_dirt_tiles])
def step_hook(self) -> (List[dict], dict):
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 self._actions.is_moving_action(agent.temp_action):
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))
if new_pos_dirt := self[c.DIRT].by_pos(agent.pos):
new_pos_dirt.set_new_amount(max(0, new_pos_dirt.amount + smeared_dirt))
else:
if self[c.DIRT].spawn_dirt(agent.tile):
new_pos_dirt = self[c.DIRT].by_pos(agent.pos)
new_pos_dirt.set_new_amount(max(0, new_pos_dirt.amount + smeared_dirt))
if self._next_dirt_spawn < 0:
pass # No Dirt Spawn
elif not self._next_dirt_spawn:
self.trigger_dirt_spawn()
self._next_dirt_spawn = self.dirt_prop.spawn_frequency
else:
self._next_dirt_spawn -= 1
return super_reward_info
def do_additional_actions(self, agent: Agent, action: Action) -> (dict, dict):
action_result = super().do_additional_actions(agent, action)
if action_result is None:
if action == a.CLEAN_UP:
return self.do_cleanup_action(agent)
else:
return None
else:
return action_result
def reset_hook(self) -> None:
super().reset_hook()
self.trigger_dirt_spawn(initial_spawn=True)
self._next_dirt_spawn = self.dirt_prop.spawn_frequency if self.dirt_prop.spawn_frequency else -1
def check_additional_done(self) -> (bool, dict):
super_done, super_dict = super().check_additional_done()
if self.dirt_prop.done_when_clean:
if all_cleaned := len(self[c.DIRT]) == 0:
super_dict.update(ALL_CLEAN_DONE=all_cleaned)
return all_cleaned, super_dict
return super_done, super_dict
def observations_hook(self) -> Dict[str, np.typing.ArrayLike]:
additional_observations = super().observations_hook()
additional_observations.update({c.DIRT: self[c.DIRT].as_array()})
return additional_observations
def gather_additional_info(self, agent: Agent) -> dict:
event_reward_dict = super().per_agent_reward_hook(agent)
info_dict = dict()
dirt = [dirt.amount for dirt in self[c.DIRT]]
current_dirt_amount = sum(dirt)
dirty_tile_count = len(dirt)
# if dirty_tile_count:
# dirt_distribution_score = entropy(softmax(np.asarray(dirt)) / dirty_tile_count)
# else:
# dirt_distribution_score = 0
info_dict.update(dirt_amount=current_dirt_amount)
info_dict.update(dirty_tile_count=dirty_tile_count)
event_reward_dict.update({'info': info_dict})
return event_reward_dict
if __name__ == '__main__':
from environments.utility_classes import AgentRenderOptions as aro
render = True
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
)
obs_props = ObservationProperties(render_agents=aro.COMBINED, omit_agent_self=True,
pomdp_r=2, additional_agent_placeholder=None, cast_shadows=True,
indicate_door_area=False)
move_props = {'allow_square_movement': True,
'allow_diagonal_movement': False,
'allow_no_op': False}
import time
global_timings = []
for i in range(10):
factory = DirtFactory(n_agents=10, done_at_collision=False,
level_name='rooms', max_steps=1000,
doors_have_area=False,
obs_prop=obs_props, parse_doors=True,
verbose=True,
mv_prop=move_props, dirt_prop=dirt_props,
# inject_agents=[TSPDirtAgent],
)
factory.save_params(Path('rewards_param'))
# noinspection DuplicatedCode
n_actions = factory.action_space.n - 1
_ = factory.observation_space
obs_space = factory.observation_space
obs_space_named = factory.named_observation_space
action_space_named = factory.named_action_space
times = []
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()
# tsp_agent = factory.get_injected_agents()[0]
rwrd = 0
for agent_i_action in random_actions:
# agent_i_action = tsp_agent.predict()
env_state, step_rwrd, done_bool, info_obj = factory.step(agent_i_action)
rwrd += step_rwrd
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('Mean Time Taken: ', sum(times) / 10)
global_timings.extend(times)
print('Mean Time Taken: ', sum(global_timings) / len(global_timings))
print('Median Time Taken: ', global_timings[len(global_timings)//2])
pass