from collections import OrderedDict
from typing import List, Union, NamedTuple
import random
import numpy as np
from environments.factory.base_factory import BaseFactory
from environments import helpers as h
from environments.factory.renderer import Renderer, Entity
from environments.utility_classes import AgentState, MovementProperties
DIRT_INDEX = -1
CLEAN_UP_ACTION = 'clean_up'
class DirtProperties(NamedTuple):
clean_amount: int = 2 # How much does the robot clean with one action.
max_spawn_ratio: float = 0.2 # On max how much tiles does the dirt spawn in percent.
gain_amount: float = 0.5 # How much dirt does spawn per tile
spawn_frequency: int = 5 # Spawn Frequency in Steps
max_local_amount: int = 1 # Max dirt amount per tile.
max_global_amount: int = 20 # Max dirt amount in the whole environment.
# noinspection PyAttributeOutsideInit
class SimpleFactory(BaseFactory):
@property
def additional_actions(self) -> Union[str, List[str]]:
return CLEAN_UP_ACTION
def _is_clean_up_action(self, action: Union[str, int]):
if isinstance(action, str):
action = self._actions.by_name(action)
return self._actions[action] == CLEAN_UP_ACTION
def __init__(self, *args, dirt_properties: DirtProperties, verbose=False, **kwargs):
self.dirt_properties = dirt_properties
self.verbose = verbose
self.max_dirt = 20
super(SimpleFactory, self).__init__(*args, additional_slices='dirt', **kwargs)
self._renderer = None # expensive - don't use it when not required !
def render(self):
if not self._renderer: # lazy init
height, width = self._state.shape[1:]
self._renderer = Renderer(width, height, view_radius=self.pomdp_radius, fps=5)
dirt = [Entity('dirt', [x, y], min(0.15 + self._state[DIRT_INDEX, x, y], 1.5), 'scale')
for x, y in np.argwhere(self._state[DIRT_INDEX] > h.IS_FREE_CELL)]
walls = [Entity('wall', pos) for pos in np.argwhere(self._state[h.LEVEL_IDX] > h.IS_FREE_CELL)]
def asset_str(agent):
if any([x is None for x in [self._state_slices[j] for j in agent.collisions]]):
print('error')
cols = ' '.join([self._state_slices[j] for j in agent.collisions])
if 'agent' in cols:
return 'agent_collision', 'blank'
elif not agent.action_valid or 'level' in cols or 'agent' in cols:
return 'agent', 'invalid'
elif self._is_clean_up_action(agent.action):
return 'agent', 'valid'
else:
return 'agent', 'idle'
agents = []
for i, agent in enumerate(self._agent_states):
name, state = asset_str(agent)
agents.append(Entity(name, agent.pos, 1, 'none', state, i+1))
self._renderer.render(dirt+walls+agents)
def spawn_dirt(self) -> None:
if not np.argwhere(self._state[DIRT_INDEX] != h.IS_FREE_CELL).shape[0] > self.dirt_properties.max_global_amount:
free_for_dirt = self.free_cells(excluded_slices=DIRT_INDEX)
# randomly distribute dirt across the grid
n_dirt_tiles = int(random.uniform(0, self.dirt_properties.max_spawn_ratio) * len(free_for_dirt))
for x, y in free_for_dirt[:n_dirt_tiles]:
new_value = self._state[DIRT_INDEX, x, y] + self.dirt_properties.gain_amount
self._state[DIRT_INDEX, x, y] = max(new_value, self.dirt_properties.max_local_amount)
else:
pass
def clean_up(self, pos: (int, int)) -> ((int, int), bool):
new_dirt_amount = self._state[DIRT_INDEX][pos] - self.dirt_properties.clean_amount
cleanup_was_sucessfull: bool
if self._state[DIRT_INDEX][pos] == h.IS_FREE_CELL:
cleanup_was_sucessfull = False
return pos, cleanup_was_sucessfull
else:
cleanup_was_sucessfull = True
self._state[DIRT_INDEX][pos] = max(new_dirt_amount, h.IS_FREE_CELL)
return pos, cleanup_was_sucessfull
def step(self, actions):
_, reward, done, info = super(SimpleFactory, self).step(actions)
if not self._next_dirt_spawn:
self.spawn_dirt()
self._next_dirt_spawn = self.dirt_properties.spawn_frequency
else:
self._next_dirt_spawn -= 1
obs = self._get_observations()
return obs, reward, done, info
def do_additional_actions(self, agent_i: int, action: int) -> ((int, int), bool):
if action != self._actions.is_moving_action(action):
if self._is_clean_up_action(action):
agent_i_pos = self.agent_i_position(agent_i)
_, valid = self.clean_up(agent_i_pos)
return agent_i_pos, valid
else:
raise RuntimeError('This should not happen!!!')
else:
raise RuntimeError('This should not happen!!!')
def reset(self) -> (np.ndarray, int, bool, dict):
_ = super().reset() # state, reward, done, info ... =
dirt_slice = np.zeros((1, *self._state.shape[1:]))
self._state = np.concatenate((self._state, dirt_slice)) # dirt is now the last slice
self.spawn_dirt()
self._next_dirt_spawn = self.dirt_properties.spawn_frequency
obs = self._get_observations()
return obs
def calculate_reward(self, agent_states: List[AgentState]) -> (int, dict):
# TODO: What reward to use?
current_dirt_amount = self._state[DIRT_INDEX].sum()
dirty_tiles = np.argwhere(self._state[DIRT_INDEX] != h.IS_FREE_CELL).shape[0]
info_dict = dict()
try:
# penalty = current_dirt_amount
reward = 0
except (ZeroDivisionError, RuntimeWarning):
reward = 0
for agent_state in agent_states:
cols = agent_state.collisions
list_of_collisions = [self._state_slices[entity] for entity in cols
if entity != self._state_slices.by_name("dirt")]
if list_of_collisions:
self.print(f't = {self._steps}\tAgent {agent_state.i} has collisions with '
f'{list_of_collisions}')
if self._is_clean_up_action(agent_state.action):
if agent_state.action_valid:
reward += 1
self.print(f'Agent {agent_state.i} did just clean up some dirt at {agent_state.pos}.')
info_dict.update(dirt_cleaned=1)
else:
reward -= 0.01
self.print(f'Agent {agent_state.i} just tried to clean up some dirt '
f'at {agent_state.pos}, but was unsucsessfull.')
info_dict.update(failed_cleanup_attempt=1)
elif self._actions.is_moving_action(agent_state.action):
if agent_state.action_valid:
# info_dict.update(movement=1)
reward -= 0.00
else:
# info_dict.update(collision=1)
# self.print('collision')
reward -= 0.01
else:
info_dict.update(no_op=1)
reward -= 0.00
for entity in list_of_collisions:
info_dict.update({f'agent_{agent_state.i}_vs_{entity}': 1})
info_dict.update(dirt_amount=current_dirt_amount)
info_dict.update(dirty_tile_count=dirty_tiles)
self.print(f"reward is {reward}")
# Potential based rewards ->
# track the last reward , minus the current reward = potential
return reward, info_dict
def print(self, string):
if self.verbose:
print(string)
if __name__ == '__main__':
render = True
move_props = MovementProperties(allow_diagonal_movement=True, allow_square_movement=True)
dirt_props = DirtProperties()
factory = SimpleFactory(movement_properties=move_props, dirt_properties=dirt_props, n_agents=2,
combin_agent_slices_in_obs=True, omit_agent_slice_in_obs=False)
# dirt_props = DirtProperties()
# move_props = MovementProperties(allow_diagonal_movement=False, allow_no_op=False)
# factory = SimpleFactory(n_agents=2, dirt_properties=dirt_props, movement_properties=move_props, level='rooms',
# pomdp_radius=2)
n_actions = factory.action_space.n - 1
for epoch in range(100):
random_actions = [[random.randint(0, n_actions) for _ in range(factory.n_agents)] for _ in range(200)]
env_state = factory.reset()
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
print(f'Factory run {epoch} done, reward is:\n {r}')