marl-factory-grid/environments/factory/simple_factory_getting_dirty.py
steffen-illium b16f316f08 Properties
2021-05-12 12:59:40 +02:00

70 lines
2.8 KiB
Python

import numpy as np
from environments.factory.base_factory import BaseFactory
from collections import namedtuple
from typing import Iterable
from environments import helpers as h
DIRT_INDEX = -1
DirtProperties = namedtuple('DirtProperties', ['clean_amount', 'max_spawn_ratio', 'gain_amount'],
defaults=[0.25, 0.1, 0.1])
class GettingDirty(BaseFactory):
@property
def _clean_up_action(self):
return self.movement_actions + 1
def __init__(self, *args, dirt_properties:DirtProperties, **kwargs):
super(GettingDirty, self).__init__(*args, **kwargs)
self._dirt_properties = dirt_properties
self.slice_strings.update({self.state.shape[0]-1: 'dirt'})
def spawn_dirt(self) -> None:
free_for_dirt = self.free_cells
# randomly distribute dirt across the grid
n_dirt_tiles = self._dirt_properties.max_spawn_ratio * len(free_for_dirt)
for x, y in free_for_dirt[:n_dirt_tiles]:
self.state[DIRT_INDEX, x, y] += self._dirt_properties.gain_amount
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 additional_actions(self, agent_i, action) -> ((int, int), bool):
if not action == self._is_moving_action(action):
if action == self._clean_up_action:
self.clean_up()
else:
raise RuntimeError('This should not happen!!!')
def reset(self) -> None:
# ToDo: When self.reset returns the new states and stuff, use it here!
super().reset() # state, agents, ... =
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()
def calculate_reward(self, collisions_vec: np.ndarray, actions: Iterable[int], r: int) -> (int, dict):
for agent_i, cols in enumerate(collisions_vecs):
cols = np.argwhere(cols != 0).flatten()
print(f't = {self.steps}\tAgent {agent_i} has collisions with '
f'{[self.slice_strings[entity] for entity in cols]}')
return 0, {}
if __name__ == '__main__':
import random
factory = GettingDirty(n_agents=1, max_dirt=8)
random_actions = [random.randint(0, 8) for _ in range(200)]
for action in random_actions:
state, r, done, _ = factory.step(action)