import time from pathlib import Path from typing import List, Union, Dict import random import numpy as np from environments.factory.additional.dirt.dirt_collections import DirtRegister from environments.factory.additional.dirt.dirt_entity import Dirt from environments.factory.additional.dirt.dirt_util import Constants, Actions, RewardsDirt, DirtProperties from environments.factory.base.base_factory import BaseFactory from environments.factory.base.objects import Agent, Action from environments.factory.base.registers import Entities from environments.factory.base.renderer import RenderEntity from environments.utility_classes import ObservationProperties 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.step_result['action_valid'] and agent.last_pos != c.NO_POS: if self._actions.is_moving_action(agent.step_result['action_name']): if old_pos_dirt := self[c.DIRT].by_pos(agent.last_pos): if smeared_dirt := round(old_pos_dirt.amount * smear_amount, 2): old_pos_dirt.set_new_amount(max(0, old_pos_dirt.amount-smeared_dirt)) 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 post_step_hook(self) -> List[Dict[str, int]]: super_post_step = super(DirtFactory, self).post_step_hook() 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) super_post_step.append(info_dict) return super_post_step 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], ) # 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