Rework for performance
This commit is contained in:
Steffen Illium
@ -8,6 +8,7 @@ import numpy as np
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# from algorithms.TSP_dirt_agent import TSPDirtAgent
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from environments.helpers import Constants as BaseConstants
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from environments.helpers import EnvActions as BaseActions
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from environments.helpers import Rewards as BaseRewards
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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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@ -21,8 +22,14 @@ class Constants(BaseConstants):
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DIRT = 'Dirt'
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class EnvActions(BaseActions):
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CLEAN_UP = 'clean_up'
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class Actions(BaseActions):
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CLEAN_UP = 'do_cleanup_action'
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class Rewards(BaseRewards):
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CLEAN_UP_VALID = 0.5
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CLEAN_UP_FAIL = -0.1
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CLEAN_UP_LAST_PIECE = 4.5
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class DirtProperties(NamedTuple):
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@ -41,10 +48,6 @@ class DirtProperties(NamedTuple):
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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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@ -116,6 +119,8 @@ def entropy(x):
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c = Constants
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a = Actions
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r = Rewards
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# noinspection PyAttributeOutsideInit, PyAbstractClass
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@ -125,7 +130,7 @@ class DirtFactory(BaseFactory):
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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(str_ident=EnvActions.CLEAN_UP))
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super_actions.append(Action(str_ident=a.CLEAN_UP))
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return super_actions
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@property
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@ -151,7 +156,7 @@ class DirtFactory(BaseFactory):
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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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def do_cleanup_action(self, agent: Agent) -> (dict, dict):
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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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@ -159,9 +164,21 @@ class DirtFactory(BaseFactory):
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self[c.DIRT].delete_env_object(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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valid = c.VALID
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self.print(f'{agent.name} did just clean up some dirt at {agent.pos}.')
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info_dict = {f'{agent.name}_{a.CLEAN_UP}_VALID': 1}
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reward = r.CLEAN_UP_VALID
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else:
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return c.NOT_VALID
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valid = c.NOT_VALID
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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 = {f'{agent.name}_{a.CLEAN_UP}_FAIL': 1}
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reward = r.CLEAN_UP_FAIL
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if valid and self.dirt_prop.done_when_clean and (len(self[c.DIRT]) == 0):
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reward += r.CLEAN_UP_LAST_PIECE
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self.print(f'{agent.name} picked up the last piece of dirt!')
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info_dict = {f'{agent.name}_{a.CLEAN_UP}_LAST_PIECE': 1}
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return valid, dict(value=reward, reason=a.CLEAN_UP, info=info_dict)
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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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@ -177,8 +194,8 @@ class DirtFactory(BaseFactory):
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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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def do_additional_step(self) -> (List[dict], dict):
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super_reward_info = 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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@ -199,42 +216,44 @@ class DirtFactory(BaseFactory):
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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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return super_reward_info
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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 == EnvActions.CLEAN_UP:
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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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def do_additional_actions(self, agent: Agent, action: Action) -> (dict, dict):
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action_result = super().do_additional_actions(agent, action)
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if action_result is None:
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if action == a.CLEAN_UP:
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return self.do_cleanup_action(agent)
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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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return action_result
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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 check_additional_done(self) -> (bool, dict):
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super_done, super_dict = super().check_additional_done()
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if self.dirt_prop.done_when_clean:
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if all_cleaned := len(self[c.DIRT]) == 0:
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super_dict.update(ALL_CLEAN_DONE=all_cleaned)
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return all_cleaned, super_dict
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return super_done, super_dict
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def _additional_observations(self) -> Dict[str, np.typing.ArrayLike]:
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additional_observations = super()._additional_observations()
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additional_observations.update({c.DIRT: self[c.DIRT].as_array()})
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return additional_observations
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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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def gather_additional_info(self, agent: Agent) -> dict:
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event_reward_dict = super().additional_per_agent_reward(agent)
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info_dict = dict()
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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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@ -242,33 +261,14 @@ class DirtFactory(BaseFactory):
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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 == EnvActions.CLEAN_UP:
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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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info_dict.update(dirt_cleaned=1)
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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 += 4.5
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info_dict.update(done_clean=1)
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self.print(f'{agent.name} did just clean up some dirt at {agent.pos}.')
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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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event_reward_dict.update({'info': info_dict})
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return event_reward_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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render = False
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dirt_props = DirtProperties(
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initial_dirt_ratio=0.35,
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@ -289,14 +289,15 @@ if __name__ == '__main__':
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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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import time
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global_timings = []
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for i in range(20):
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for i in range(10):
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factory = DirtFactory(n_agents=2, done_at_collision=False,
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level_name='rooms', max_steps=1000,
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doors_have_area=False,
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obs_prop=obs_props, parse_doors=True,
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record_episodes=True, verbose=True,
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verbose=False,
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mv_prop=move_props, dirt_prop=dirt_props,
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# inject_agents=[TSPDirtAgent],
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)
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@ -307,7 +308,6 @@ if __name__ == '__main__':
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obs_space = factory.observation_space
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obs_space_named = factory.named_observation_space
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times = []
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import time
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for epoch in range(10):
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start_time = time.time()
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random_actions = [[random.randint(0, n_actions) for _
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@ -318,18 +318,19 @@ if __name__ == '__main__':
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factory.render()
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# tsp_agent = factory.get_injected_agents()[0]
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r = 0
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rwrd = 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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env_state, step_rwrd, done_bool, info_obj = factory.step(agent_i_action)
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rwrd += step_rwrd
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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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times.append(time.time() - start_time)
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# print(f'Factory run {epoch} done, reward is:\n {r}')
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print('Time Taken: ', sum(times) / 10)
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global_timings.append(sum(times) / 10)
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print('Time Taken: ', sum(global_timings[10:]) / 10)
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print('Mean Time Taken: ', sum(times) / 10)
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global_timings.extend(times)
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print('Mean Time Taken: ', sum(global_timings) / len(global_timings))
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print('Median Time Taken: ', global_timings[len(global_timings)//2])
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pass
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