627 lines
19 KiB
Python
627 lines
19 KiB
Python
from os import path
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import heapq
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import copy
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from settings import *
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from sprites import Direction
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class PlanRoute():
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""" The problem of moving the Agent from one place to other """
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def __init__(self, initial, goal, allowed, puddles=None, dimrow=None):
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""" Define goal state and initialize a problem """
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self.initial = initial
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self.goal = goal
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self.dimrow = dimrow
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self.goal = goal
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self.allowed = allowed
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self.puddles = puddles
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def actions(self, state):
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possible_actions = ['Forward', 'Left', 'Right']
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x, y = state.get_location()
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orientation = state.get_orientation()
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# Prevent Bumps
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if y == 0 and orientation == 'LEFT':
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if 'Forward' in possible_actions:
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possible_actions.remove('Forward')
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if x == 0 and orientation == 'DOWN':
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if 'Forward' in possible_actions:
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possible_actions.remove('Forward')
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if y == self.dimrow and orientation == 'RIGHT':
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if 'Forward' in possible_actions:
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possible_actions.remove('Forward')
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if x == self.dimrow and orientation == 'UP':
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if 'Forward' in possible_actions:
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possible_actions.remove('Forward')
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return possible_actions
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def result(self, state, action):
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""" Given state and action, return a new state that is the result of the action.
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Action is assumed to be a valid action in the state """
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x, y = state.get_location()
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proposed_loc = list()
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#proposed_loc = []
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# Move Forward
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if action == 'Forward':
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if state.get_orientation() == 'UP':
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proposed_loc = [x + 1, y]
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elif state.get_orientation() == 'DOWN':
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proposed_loc = [x - 1, y]
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elif state.get_orientation() == 'LEFT':
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proposed_loc = [x, y - 1]
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elif state.get_orientation() == 'RIGHT':
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proposed_loc = [x, y + 1]
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else:
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raise Exception('InvalidOrientation')
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# Rotate counter-clockwise
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elif action == 'Right':
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if state.get_orientation() == 'UP':
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state.set_orientation('LEFT')
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elif state.get_orientation() == 'DOWN':
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state.set_orientation('RIGHT')
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elif state.get_orientation() == 'LEFT':
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state.set_orientation('DOWN')
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elif state.get_orientation() == 'RIGHT':
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state.set_orientation('UP')
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else:
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raise Exception('InvalidOrientation')
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# Rotate clockwise
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elif action == 'Left':
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if state.get_orientation() == 'UP':
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state.set_orientation('RIGHT')
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elif state.get_orientation() == 'DOWN':
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state.set_orientation('LEFT')
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elif state.get_orientation() == 'LEFT':
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state.set_orientation('UP')
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elif state.get_orientation() == 'RIGHT':
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state.set_orientation('DOWN')
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else:
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raise Exception('InvalidOrientation')
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if(proposed_loc):
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tupled_proposed_loc = tuple([proposed_loc[0], proposed_loc[1]])
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if tupled_proposed_loc in self.allowed:
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state.set_location(proposed_loc[0], proposed_loc[1])
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return state
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def goal_test(self, state):
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""" Given a state, return True if state is a goal state or False, otherwise """
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return state.get_location() == self.goal.get_location()
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def path_cost(self, c, state1, action, state2):
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if action == "Forward" or action == "Left" or action == "Right":
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x1, y1 = state1.get_location()
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location1 = tuple([x1, y1])
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x2, y2 = state2.get_location()
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location2 = tuple([x1, y1])
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if location2 in self.puddles:
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return c + 2
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if location1 == location2 and state1 in self.puddles:
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return c + 2
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return c+1
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def h(self, node):
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""" Return the heuristic value for a given state."""
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# Manhattan Heuristic Function
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x1, y1 = node.state.get_location()
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x2, y2 = self.goal.get_location()
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return abs(x2 - x1) + abs(y2 - y1)
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class Node:
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def __init__(self, state, parent=None, action=None, path_cost=0):
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"""Create a search tree Node, derived from a parent by an action."""
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self.state = state #AgentPosition?
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self.parent = parent
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self.action = action
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self.path_cost = path_cost
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def __repr__(self):
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return "<Node {}>".format(self.state)
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def solution(self):
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"""Return the sequence of actions to go from the root to this node."""
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return [node.action for node in self.path()[1:]]
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def expand(self, problem):
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"""List the nodes reachable in one step from this node."""
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test_node_list = [self.child_node(problem, action)
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for action in problem.actions(self.state)]
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return [self.child_node(problem, action)
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for action in problem.actions(self.state)]
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def child_node(self, problem, action):
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next_state = problem.result(copy.deepcopy(self.state), action)
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next_node = Node(next_state, self, action, problem.path_cost(
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self.path_cost, self.state, action, next_state))
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#print(problem.path_cost(
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# self.path_cost, self.state, action, next_state))
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return next_node
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def __eq__(self, other):
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return isinstance(other, Node) and self.state == other.state
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def __lt__(self, other):
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return isinstance(other, Node) and self.state == other.state
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def __hash__(self):
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# We use the hash value of the state
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# stored in the node instead of the node
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# object itself to quickly search a node
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# with the same state in a Hash Table
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return hash(self.state)
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def path(self):
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"""Return a list of nodes forming the path from the root to this node."""
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node, path_back = self, []
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while node:
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path_back.append(node)
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node = node.parent
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return list(reversed(path_back))
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class AgentPosition:
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def __init__(self, x, y, orientation):
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self.X = x
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self.Y = y
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self.orientation = orientation
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def get_location(self):
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return self.X, self.Y
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def set_location(self, x, y):
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self.X = x
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self.Y = y
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def get_orientation(self):
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return self.orientation
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def set_orientation(self, orientation):
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self.orientation = orientation
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def __eq__(self, other):
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if (other.get_location() == self.get_location() and
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other.get_orientation() == self.get_orientation()):
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return True
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else:
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return False
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def __hash__(self):
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return hash((self.X, self.Y, self.orientation))
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class SweeperAgent:
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def __init__(self, dimensions=None):
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self.dimrow = dimensions
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self.current_position = None
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self.orientation = ""
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self.initial = set()
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self.goal = set()
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self.allowed_points = set()
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self.puddle_points = set()
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def where_am_i(self, map_name = "map.txt"):
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temp_map = [list(item) for item in SweeperAgent.loadMap(map_name)]
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for row in range(MAP_SIZE):
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for column, pos in enumerate(temp_map[row]):
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if pos == ">" or pos == "<" or pos == "^" or pos == "v":
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self.row = row
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self.column = column
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return row, column
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# add orientation
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def where_to_go(self, map_name = "goal_map.txt"):
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temp_map = [list(item) for item in SweeperAgent.loadMap(map_name)]
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for row in range(MAP_SIZE):
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for column, pos in enumerate(temp_map[row]):
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if pos == ">" or pos == "<" or pos == "^" or pos == "v":
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self.row = row
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self.column = column
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return row, column
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@staticmethod
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def set_allowed(allowed_points, map_name = 'map.txt'):
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temp_map = [list(item) for item in SweeperAgent.loadMap(map_name)]
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a_row = 0
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a_column = 0
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for row in range(MAP_SIZE):
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for column, pos in enumerate(temp_map[row]):
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if pos == "." or pos == 'p' or pos == '>' or pos == '<' or pos == 'v' or pos == '^':
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a_row = row
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a_column = column
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location = tuple([a_row, a_column])
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allowed_points.add(location)
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@staticmethod
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def set_allowed_for_genetic(allowed_points, map_location):
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temp_map = [list(item) for item in SweeperAgent.loadMap(map_location)]
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a_row = 0
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a_column = 0
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for row in range(MAP_SIZE):
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for column, pos in enumerate(temp_map[row]):
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if pos == "." or pos == 'x':
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a_row = row
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a_column = column
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location = tuple([a_row, a_column])
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allowed_points.add(location)
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@staticmethod
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def set_puddles(puddle_points, map_name = 'map.txt'):
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temp_map = [list(item) for item in SweeperAgent.loadMap(map_name)]
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a_row = 0
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a_column = 0
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for row in range(MAP_SIZE):
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for column, pos in enumerate(temp_map[row]):
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if pos == "p" :
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a_row = row
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a_column = column
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location = tuple([a_row, a_column])
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puddle_points.add(location)
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@staticmethod
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def get_goal(map_name = 'goal_map.txt'):
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temp_map = [list(item) for item in SweeperAgent.loadMap(map_name)]
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a_row = 0
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a_column = 0
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for row in range(MAP_SIZE):
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for column, pos in enumerate(temp_map[row]):
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if pos == '>' or pos == '<' or pos == 'v' or pos == '^':
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a_row = row
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a_column = column
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return a_row, a_column
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@staticmethod
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def set_initial(initial):
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temp_map = [list(item) for item in SweeperAgent.loadMap('map.txt')]
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a_row = 0
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a_column = 0
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for row in range(MAP_SIZE):
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for column, pos in enumerate(temp_map[row]):
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if pos == '>' or pos == '<' or pos == 'v' or pos == '^':
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a_row = row
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a_column = column
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location = tuple([a_row, a_column])
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initial.add(location)
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@staticmethod
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def set_orientation(map_name = 'map.txt'):
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temp_map = [list(item) for item in SweeperAgent.loadMap(map_name)]
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orientation = ""
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for row in range(MAP_SIZE):
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for column, pos in enumerate(temp_map[row]):
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if pos == ">":
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orientation = "RIGHT"
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if pos == "<":
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orientation = "LEFT"
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if pos == "^":
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orientation = "UP"
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if pos == "v":
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orientation = "DOWN"
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return orientation
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@staticmethod
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def set_goal_orientation(map_name = 'goal_map.txt'):
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temp_map = [list(item) for item in SweeperAgent.loadMap(map_name)]
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orientation = ""
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for row in range(MAP_SIZE):
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for column, pos in enumerate(temp_map[row]):
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if pos == ">":
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orientation = "RIGHT"
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if pos == "<":
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orientation = "LEFT"
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if pos == "^":
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orientation = "UP"
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if pos == "v":
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orientation = "DOWN"
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return orientation
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@staticmethod
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def run_manual(self,
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given_orientation,
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given_goal_orientation,
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x_my_location,
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y_my_location,
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x_goal_location,
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y_goal_location,
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map_location
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):
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self.orientation = given_orientation
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goal_orientation = given_goal_orientation
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SweeperAgent.set_allowed(self.set_allowed_for_genetic(map_location))
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x = x_my_location
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y = y_my_location
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x1 = x_goal_location
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x2 = y_goal_location
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agent_position = AgentPosition(x, y, self.orientation)
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goal_position = AgentPosition(x1, x2, goal_orientation)
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return len(self.plan_route(agent_position, goal_position, self.allowed_points, self.puddle_points))
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@staticmethod
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def run_manual_map(self,start_map, goal_map):
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self.orientation = SweeperAgent.set_orientation(start_map)
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goal_orientation = SweeperAgent.set_goal_orientation(goal_map)
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SweeperAgent.set_allowed(self.allowed_points, start_map)
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SweeperAgent.set_puddles(self.puddle_points, start_map)
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x, y = self.where_am_i(start_map)
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x1, y1 = SweeperAgent.get_goal(goal_map)
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agent_position = AgentPosition(x, y, self.orientation)
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goal_position = AgentPosition(x1, y1, goal_orientation)
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return self.plan_route(agent_position, goal_position, self.allowed_points, self.puddle_points)
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@staticmethod
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def run(self):
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self.orientation = SweeperAgent.set_orientation()
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goal_orientation = SweeperAgent.set_goal_orientation()
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#SweeperAgent.set_initial(self.initial)
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#SweeperAgent.set_goal(self.goal)
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SweeperAgent.set_allowed(self.allowed_points)
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SweeperAgent.set_puddles(self.puddle_points)
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x, y = self.where_am_i()
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x1, y1 = SweeperAgent.get_goal()
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agent_position = AgentPosition(x, y, self.orientation)
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goal_position = AgentPosition(x1, y1, goal_orientation)
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return self.plan_route(agent_position, goal_position, self.allowed_points, self.puddle_points)
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"""print("allowed: ")
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print("(row, column)")
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print(sorted(self.allowed_points))
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print("puddles:")
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print(sorted(self.puddle_points))
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print("initial:")
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print(self.initial)
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print("goal:")
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print(self.goal)
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print("orientation:")
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print(self.orientation)"""
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def plan_route(self, current, goals, allowed, puddles):
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problem = PlanRoute(current, goals, allowed, puddles, MAP_SIZE-1)
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return SweeperAgent.astar_search(problem, problem.h)
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#return SweeperAgent.astar_search(problem, problem.h)
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"""TODO"""
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# liczenie kosztów
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#
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@staticmethod
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def loadMap(map_name=''):
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maze = []
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map_folder = path.dirname(__file__)
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with open(path.join(map_folder, map_name), 'rt') as f:
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for line in f:
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maze.append(line.rstrip('\n'))
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# print(maze)
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return maze
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@staticmethod
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def astar_search(problem, h=None):
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"""A* search is best-first graph search with f(n) = g(n)+h(n).
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You need to specify the h function when you call astar_search, or
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else in your Problem subclass."""
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# h = memoize(h or problem.h, 'h')
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return SweeperAgent.best_first_graph_search(problem, lambda n: n.path_cost + h(n))
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#return best_first_graph_search(problem)
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@staticmethod
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#def best_first_graph_search(problem, f, display=False):
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def best_first_graph_search(problem, f, display=True):
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"""TODO"""
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# Zaimplementować klasę Node dla Astar
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history = []
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node = Node(problem.initial)
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frontier = PriorityQueue('min', f)
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frontier.append(node)
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explored = set()
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while frontier:
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node = frontier.pop()
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if problem.goal_test(node.state):
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if display:
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print(len(explored), "paths have been expanded and", len(frontier), "paths remain in the frontier")
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while(node.parent != None):
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history.append(node.action)
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node = node.parent
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#return child
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history.reverse()
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print(history)
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return history
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#return history
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#break
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#return node
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#break
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explored.add(copy.deepcopy(node.state))
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test_child_chamber = node.expand(problem)
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for child in node.expand(problem):
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if child.state not in explored and child not in frontier:
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frontier.append(copy.deepcopy(child))
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elif child in frontier:
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if f(child) < frontier[child]:
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del frontier[child]
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frontier.append(child)
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return history
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#return None
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class PriorityQueue:
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"""A Queue in which the minimum (or maximum) element (as determined by f and
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order) is returned first.
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If order is 'min', the item with minimum f(x) is
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returned first; if order is 'max', then it is the item with maximum f(x).
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Also supports dict-like lookup."""
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def __init__(self, order='min', f=lambda x: x):
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self.heap = []
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if order == 'min':
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self.f = f
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elif order == 'max': # now item with max f(x)
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self.f = lambda x: -f(x) # will be popped first
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else:
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raise ValueError("Order must be either 'min' or 'max'.")
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def append(self, item):
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"""Insert item at its correct position."""
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heapq.heappush(self.heap, (self.f(item), item))
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def extend(self, items):
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"""Insert each item in items at its correct position."""
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for item in items:
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|
self.append(item)
|
|
|
|
def pop(self):
|
|
"""Pop and return the item (with min or max f(x) value)
|
|
depending on the order."""
|
|
if self.heap:
|
|
return heapq.heappop(self.heap)[1]
|
|
else:
|
|
raise Exception('Trying to pop from empty PriorityQueue.')
|
|
|
|
def __len__(self):
|
|
"""Return current capacity of PriorityQueue."""
|
|
return len(self.heap)
|
|
|
|
def __contains__(self, key):
|
|
"""Return True if the key is in PriorityQueue."""
|
|
return any([item == key for _, item in self.heap])
|
|
|
|
def __getitem__(self, key):
|
|
"""Returns the first value associated with key in PriorityQueue.
|
|
Raises KeyError if key is not present."""
|
|
for value, item in self.heap:
|
|
if item == key:
|
|
return value
|
|
raise KeyError(str(key) + " is not in the priority queue")
|
|
|
|
def __delitem__(self, key):
|
|
"""Delete the first occurrence of key."""
|
|
try:
|
|
del self.heap[[item == key for _, item in self.heap].index(True)]
|
|
except ValueError:
|
|
raise KeyError(str(key) + " is not in the priority queue")
|
|
heapq.heapify(self.heap)
|
|
|
|
|
|
class Test:
|
|
@staticmethod
|
|
def run():
|
|
|
|
allowed_points = set()
|
|
puddle_points = set()
|
|
|
|
initial = set()
|
|
goal = set()
|
|
|
|
orientation = SweeperAgent.set_orientation()
|
|
|
|
SweeperAgent.set_initial(initial)
|
|
SweeperAgent.set_goal(goal)
|
|
|
|
SweeperAgent.set_allowed(allowed_points)
|
|
SweeperAgent.set_puddles(puddle_points)
|
|
|
|
|
|
|
|
print("allowed: ")
|
|
print("(row, column)")
|
|
print(sorted(allowed_points))
|
|
print("puddles:")
|
|
print(sorted(puddle_points))
|
|
print("initial:")
|
|
print(initial)
|
|
print("goal:")
|
|
print(goal)
|
|
print("orientation:")
|
|
print(orientation)
|
|
|
|
|