def choose_next_option(self, sim=HurricaneSimulator()):
self.set_state(sim.get_state())
precalculated_answer = self.check_if_path_ready(sim)
if precalculated_answer != -1:
return precalculated_answer
best_state = self.build_full_state(sim)
best_sim = copy.deepcopy(sim)
tree = Node(state=best_state, sim=sim)
best_child = tree
best_path = list()
nodes_not_expanded = [tree]
current_tree_node = tree
expands_in_this_search = 0
while not self.is_goal(best_state, best_sim) and self.max_expands > expands_in_this_search:
""" Expand the current node """
nodes_not_expanded.remove(current_tree_node)
""" Explore all options"""
actions = self.get_all_possible_actions(best_sim)
for action in actions:
sim_emulation = copy.deepcopy(best_sim) # Not actually required for greedy search, but for A*
sim_emulation.apply_action(action[0])
nodes_not_expanded.append(Node(state=self.build_full_state(sim_emulation), sim=sim_emulation,
g=current_tree_node.g_score + action[1],
h=self.calculate_heuristic_for_action(action[1], sim_emulation),
p=current_tree_node))
""" Find best child """
best_child = self.find_best_branch_to_explore(nodes_not_expanded)
best_state = best_child.state
best_sim = best_child.sim
current_tree_node = best_child
self.expanded_nodes += 1
expands_in_this_search += 1
""" No possible way to expand """
if best_child == tree:
return -1
current_tree_node = best_child
while current_tree_node != tree:
best_path.insert(0, current_tree_node.state)
current_tree_node = current_tree_node.parent
self.path = best_path
ans = self.path.pop(0)['state']
return ans
def is_goal(full_state, sim=HurricaneSimulator()):
"""
Is a goal reached?
A goal is run out the clock or best goal is saved all people
:param full_state: as built by buil_full_state() method
:param sim:
:return: true if goal or false
"""
if full_state['number_of_people_to_save'] == 0:
""" Reached optimal goal"""
return True
if full_state['time_passed'] >= sim.deadline:
""" Run out of time :( """
return True
return False
def is_new_action_better(best_value,
new_value,
sim_emulator=HurricaneSimulator()):
"""
Adversarial (zero sum game): each agent aims to maximize its own score minus the opposing agent's score
"""
cur_best = best_value[sim_emulator.agent_index] - best_value[
1 - sim_emulator.agent_index]
if isinf(best_value[1 - sim_emulator.agent_index]
) and best_value[1 - sim_emulator.agent_index] < 0:
'" If the second score is - infinity, set the subtraction to be - infinity "'
cur_best = -inf
return new_value[sim_emulator.agent_index] - new_value[
1 - sim_emulator.agent_index] > cur_best
def calculate_heuristic_for_action(self, cost, sim=HurricaneSimulator()):
"""
This function calculates the heuristic for a single.
:param vertex: the vertex to which you wish to calculate the heuristic
:param sim: the environment
:return: a heuristic value
"""
number_of_people_in_towns = sim.get_number_of_people_in_towns()
people_unable_to_save = number_of_people_in_towns + self.people_in_vehicle
cost_to_people = self.find_people(
sim) # Find costs for all vertices with people
cost_to_shelter = self.find_sheleter(
sim) # Find costs for all shelter vertices
if len(cost_to_shelter) == 0:
""" No shelter, we are all doomed :("""
return cost + people_unable_to_save * PEOPLE_UNSAVED_VALUE
""" Find closest shelter """
closest_shelter = find_closest(cost_to_shelter)[1]
if (1 + sim.K * self.people_in_vehicle
) * closest_shelter + sim.get_time() < sim.deadline:
""" People in vehicle can be saved """
people_unable_to_save -= self.people_in_vehicle
else:
""" Unable to reach shelter """
return cost + people_unable_to_save * PEOPLE_UNSAVED_VALUE
""" Find people in towns """
shelter_nodes = sim.get_shelter()
time_elapsed = sim.get_time()
for people in cost_to_people:
path_to_shelter_for_person = find_closest(
self.search_grid(shelter_nodes, people[2], sim))
time_elapsed_for_person = time_elapsed + people[1] + \
(1 + sim.K * sim.graph[people[2]][people[2]].num_of_people) \
* path_to_shelter_for_person[1]
if time_elapsed_for_person < sim.deadline:
people_unable_to_save -= sim.graph[people[2]][
people[2]].num_of_people
return cost + people_unable_to_save * PEOPLE_UNSAVED_VALUE
def search_grid(self, grid, starting_index, sim=HurricaneSimulator()):
"""
This function performs the actual search in a grid, and calculates the cost to each vertex of interest
:param grid: people or shelter grid
:param sim: the environment
:return: list of tuples: (next vertex, cost to final destination, final destination)
"""
l_cost = []
weight_grid = sim.get_weights()
for i in range(sim.num_of_vertices):
if grid[i][i]:
path, depth_searched = dijkstra_shortest_path(
weight_grid, starting_index, i)
# self.steps_explored += depth_searched
if len(path) == 1:
""" Already at a shelter town """
l_cost.append((path[0][0], 0, path[0][0]))
elif path[-1][1] != -1:
l_cost.append(
(path[1][0], path[-1][1], path[-1][0])
) # set the cost for the entire path and the next step
return l_cost
def build_full_state(sim=HurricaneSimulator()):
"""
Build a full state to work with
:param sim: the env
:return: a dict with (state, time, list_of_vertices_with_people, number_of_people_to_save, number_of_people_in_vehicle)
"""
""" Create a list of people vertices """
list_of_vertices_with_people = []
people = sim.get_people()
sum_of_people = 0
for i in range(sim.num_of_vertices):
sum_of_people += people[i][i]
if people[i][i]:
data = (i, people[i][i])
list_of_vertices_with_people.append(data)
""" Create the full state """
full_state = {
'state': sim.get_state(),
'time_passed': sim.get_time(),
'list_of_vertices_with_people': list_of_vertices_with_people,
'number_of_people_to_save': sum_of_people + sim.people_in_vehicle,
'number_of_people_in_vehicle': sim.people_in_vehicle
}
return full_state
def choose_next_option(self, sim=HurricaneSimulator()):
print('You are at: ', self.get_state())
next_state = input("Choose next step:")
return int(next_state)
def is_new_action_better(self, best_value, new_value, sim_emulator=HurricaneSimulator()):
"""
A fully cooperative both agents aim to maximize the sum of scores.
"""
return sum(new_value) > sum(best_value)
def choose_next_option(self, sim=HurricaneSimulator()):
raise NotImplementedError("")
def value(sim=HurricaneSimulator()):
""" How many people were saved by each agent """
return [agent.people_saved for agent in sim.all_agents]
def choose_next_option(self, sim=HurricaneSimulator()):
if self.is_terminal(sim):
return -1
best = self.recursive_tree(0, self.is_zero_sum, sim)
return best[0]
