300 lines
12 KiB
Python
300 lines
12 KiB
Python
import time
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from enum import Enum
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from typing import List, Union, NamedTuple, Dict
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import random
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import numpy as np
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from environments.helpers import Constants as c
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from environments import helpers as h
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from environments.factory.base.base_factory import BaseFactory
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from environments.factory.base.objects import Agent, Action, Entity, Tile
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from environments.factory.base.registers import Entities, MovingEntityObjectRegister
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from environments.factory.base.renderer import RenderEntity
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from environments.utility_classes import ObservationProperties
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CLEAN_UP_ACTION = h.EnvActions.CLEAN_UP
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class DirtProperties(NamedTuple):
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initial_dirt_ratio: float = 0.3 # On INIT, on max how much tiles does the dirt spawn in percent.
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initial_dirt_spawn_r_var: float = 0.05 # How much does the dirt spawn amount vary?
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clean_amount: float = 1 # How much does the robot clean with one actions.
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max_spawn_ratio: float = 0.20 # On max how much tiles does the dirt spawn in percent.
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max_spawn_amount: float = 0.3 # How much dirt does spawn per tile at max.
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spawn_frequency: int = 0 # Spawn Frequency in Steps.
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max_local_amount: int = 2 # Max dirt amount per tile.
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max_global_amount: int = 20 # Max dirt amount in the whole environment.
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dirt_smear_amount: float = 0.2 # Agents smear dirt, when not cleaning up in place.
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agent_can_interact: bool = True # Whether the agents can interact with the dirt in this environment.
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done_when_clean = True
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class Dirt(Entity):
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@property
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def can_collide(self):
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return False
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@property
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def amount(self):
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return self._amount
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def encoding(self):
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# Edit this if you want items to be drawn in the ops differntly
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return self._amount
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def __init__(self, *args, amount=None, **kwargs):
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super(Dirt, self).__init__(*args, **kwargs)
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self._amount = amount
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def set_new_amount(self, amount):
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self._amount = amount
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def summarize_state(self, **kwargs):
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state_dict = super().summarize_state(**kwargs)
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state_dict.update(amount=float(self.amount))
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return state_dict
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class DirtRegister(MovingEntityObjectRegister):
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def as_array(self):
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if self._array is not None:
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self._array[:] = c.FREE_CELL.value
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for dirt in list(self.values()):
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if dirt.amount == 0:
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self.delete_item(dirt)
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self._array[0, dirt.x, dirt.y] = dirt.amount
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else:
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self._array = np.zeros((1, *self._level_shape))
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return self._array
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_accepted_objects = Dirt
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@property
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def amount(self):
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return sum([dirt.amount for dirt in self])
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@property
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def dirt_properties(self):
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return self._dirt_properties
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def __init__(self, dirt_properties, *args):
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super(DirtRegister, self).__init__(*args)
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self._dirt_properties: DirtProperties = dirt_properties
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def spawn_dirt(self, then_dirty_tiles) -> c:
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if isinstance(then_dirty_tiles, Tile):
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then_dirty_tiles = [then_dirty_tiles]
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for tile in then_dirty_tiles:
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if not self.amount > self.dirt_properties.max_global_amount:
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dirt = self.by_pos(tile.pos)
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if dirt is None:
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dirt = Dirt(tile, amount=self.dirt_properties.max_spawn_amount)
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self.register_item(dirt)
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else:
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new_value = dirt.amount + self.dirt_properties.max_spawn_amount
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dirt.set_new_amount(min(new_value, self.dirt_properties.max_local_amount))
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else:
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return c.NOT_VALID
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return c.VALID
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def __repr__(self):
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s = super(DirtRegister, self).__repr__()
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return f'{s[:-1]}, {self.amount})'
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def softmax(x):
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"""Compute softmax values for each sets of scores in x."""
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e_x = np.exp(x - np.max(x))
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return e_x / e_x.sum()
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def entropy(x):
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return -(x * np.log(x + 1e-8)).sum()
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# noinspection PyAttributeOutsideInit, PyAbstractClass
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class DirtFactory(BaseFactory):
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@property
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def additional_actions(self) -> Union[Action, List[Action]]:
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super_actions = super().additional_actions
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if self.dirt_prop.agent_can_interact:
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super_actions.append(Action(enum_ident=CLEAN_UP_ACTION))
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return super_actions
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@property
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def additional_entities(self) -> Dict[(Enum, Entities)]:
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super_entities = super().additional_entities
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dirt_register = DirtRegister(self.dirt_prop, self._level_shape)
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super_entities.update(({c.DIRT: dirt_register}))
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return super_entities
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def __init__(self, *args, dirt_prop: DirtProperties = DirtProperties(), env_seed=time.time_ns(), **kwargs):
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if isinstance(dirt_prop, dict):
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dirt_prop = DirtProperties(**dirt_prop)
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self.dirt_prop = dirt_prop
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self._dirt_rng = np.random.default_rng(env_seed)
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self._dirt: DirtRegister
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kwargs.update(env_seed=env_seed)
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super().__init__(*args, **kwargs)
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def render_additional_assets(self, mode='human'):
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additional_assets = super().render_additional_assets()
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dirt = [RenderEntity('dirt', dirt.tile.pos, min(0.15 + dirt.amount, 1.5), 'scale')
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for dirt in self[c.DIRT]]
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additional_assets.extend(dirt)
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return additional_assets
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def clean_up(self, agent: Agent) -> c:
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if dirt := self[c.DIRT].by_pos(agent.pos):
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new_dirt_amount = dirt.amount - self.dirt_prop.clean_amount
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if new_dirt_amount <= 0:
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self[c.DIRT].delete_item(dirt)
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else:
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dirt.set_new_amount(max(new_dirt_amount, c.FREE_CELL.value))
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return c.VALID
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else:
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return c.NOT_VALID
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def trigger_dirt_spawn(self, initial_spawn=False):
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dirt_rng = self._dirt_rng
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free_for_dirt = [x for x in self[c.FLOOR]
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if len(x.guests) == 0 or (len(x.guests) == 1 and isinstance(next(y for y in x.guests), Dirt))
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]
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self._dirt_rng.shuffle(free_for_dirt)
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if initial_spawn:
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var = self.dirt_prop.initial_dirt_spawn_r_var
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new_spawn = self.dirt_prop.initial_dirt_ratio + dirt_rng.uniform(-var, var)
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else:
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new_spawn = dirt_rng.uniform(0, self.dirt_prop.max_spawn_ratio)
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n_dirt_tiles = max(0, int(new_spawn * len(free_for_dirt)))
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self[c.DIRT].spawn_dirt(free_for_dirt[:n_dirt_tiles])
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def do_additional_step(self) -> dict:
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info_dict = super().do_additional_step()
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if smear_amount := self.dirt_prop.dirt_smear_amount:
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for agent in self[c.AGENT]:
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if agent.temp_valid and agent.last_pos != c.NO_POS:
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if self._actions.is_moving_action(agent.temp_action):
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if old_pos_dirt := self[c.DIRT].by_pos(agent.last_pos):
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if smeared_dirt := round(old_pos_dirt.amount * smear_amount, 2):
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old_pos_dirt.set_new_amount(max(0, old_pos_dirt.amount-smeared_dirt))
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if new_pos_dirt := self[c.DIRT].by_pos(agent.pos):
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new_pos_dirt.set_new_amount(max(0, new_pos_dirt.amount + smeared_dirt))
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else:
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if self[c.DIRT].spawn_dirt(agent.tile):
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new_pos_dirt = self[c.DIRT].by_pos(agent.pos)
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new_pos_dirt.set_new_amount(max(0, new_pos_dirt.amount + smeared_dirt))
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if self._next_dirt_spawn < 0:
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pass # No Dirt Spawn
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elif not self._next_dirt_spawn:
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self.trigger_dirt_spawn()
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self._next_dirt_spawn = self.dirt_prop.spawn_frequency
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else:
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self._next_dirt_spawn -= 1
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return info_dict
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def do_additional_actions(self, agent: Agent, action: Action) -> Union[None, c]:
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valid = super().do_additional_actions(agent, action)
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if valid is None:
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if action == CLEAN_UP_ACTION:
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if self.dirt_prop.agent_can_interact:
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valid = self.clean_up(agent)
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return valid
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else:
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return c.NOT_VALID
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else:
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return None
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else:
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return valid
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def do_additional_reset(self) -> None:
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super().do_additional_reset()
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self.trigger_dirt_spawn(initial_spawn=True)
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self._next_dirt_spawn = self.dirt_prop.spawn_frequency if self.dirt_prop.spawn_frequency else -1
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def check_additional_done(self):
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super_done = super().check_additional_done()
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done = self.dirt_prop.done_when_clean and (len(self[c.DIRT]) == 0)
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return super_done or done
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def calculate_additional_reward(self, agent: Agent) -> (int, dict):
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reward, info_dict = super().calculate_additional_reward(agent)
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dirt = [dirt.amount for dirt in self[c.DIRT]]
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current_dirt_amount = sum(dirt)
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dirty_tile_count = len(dirt)
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if dirty_tile_count:
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dirt_distribution_score = entropy(softmax(np.asarray(dirt)) / dirty_tile_count)
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else:
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dirt_distribution_score = 0
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info_dict.update(dirt_amount=current_dirt_amount)
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info_dict.update(dirty_tile_count=dirty_tile_count)
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info_dict.update(dirt_distribution_score=dirt_distribution_score)
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if agent.temp_action == CLEAN_UP_ACTION:
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if agent.temp_valid:
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# Reward if pickup succeds,
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# 0.5 on every pickup
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reward += 0.5
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if self.dirt_prop.done_when_clean and (len(self[c.DIRT]) == 0):
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# 0.5 additional reward for the very last pickup
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reward += 0.5
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self.print(f'{agent.name} did just clean up some dirt at {agent.pos}.')
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info_dict.update(dirt_cleaned=1)
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else:
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reward -= 0.01
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self.print(f'{agent.name} just tried to clean up some dirt at {agent.pos}, but failed.')
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info_dict.update({f'{agent.name}_failed_dirt_cleanup': 1})
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info_dict.update(failed_dirt_clean=1)
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# Potential based rewards ->
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# track the last reward , minus the current reward = potential
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return reward, info_dict
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if __name__ == '__main__':
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from environments.utility_classes import AgentRenderOptions as ARO
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render = True
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dirt_props = DirtProperties(1, 0.05, 0.1, 3, 1, 20, 0)
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obs_props = ObservationProperties(render_agents=ARO.COMBINED, omit_agent_self=True,
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pomdp_r=15, additional_agent_placeholder=None)
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move_props = {'allow_square_movement': True,
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'allow_diagonal_movement': False,
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'allow_no_op': False}
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factory = DirtFactory(n_agents=5, done_at_collision=False,
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level_name='rooms', max_steps=400,
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obs_prop=obs_props, parse_doors=True,
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record_episodes=True, verbose=True,
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mv_prop=move_props, dirt_prop=dirt_props
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)
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# noinspection DuplicatedCode
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n_actions = factory.action_space.n - 1
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_ = factory.observation_space
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for epoch in range(4):
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random_actions = [[random.randint(0, n_actions) for _
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in range(factory.n_agents)] for _
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in range(factory.max_steps+1)]
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env_state = factory.reset()
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r = 0
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for agent_i_action in random_actions:
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env_state, step_r, done_bool, info_obj = factory.step(agent_i_action)
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r += step_r
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if render:
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factory.render()
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if done_bool:
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break
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print(f'Factory run {epoch} done, reward is:\n {r}')
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pass
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