def get_action(self, perception):
""" This is the main method for all your agents
Along with the __init__, you must at least implement this method in
all your agents to make them work properly.
This method receives a perception from the environment and returns
an action after performing the A* search with manhattan_distance as
heuristics.
"""
state = self.__state_from_perception(perception)
i,j = state[0]
grid, _ = perception
# if gas station and tank not full, refill
if grid[i][j] == self.player_number+7 and state[1] < self.tank_capacity:
return 'REFILL'
self.start_agent(perception, self.problem_reference,
tank_capacity=self.tank_capacity)
node = util.a_star(self.problem, self.heuristic)
if not node: # Search did not find any action
return 'STOP'
action = node.action
last_action = None
while node.parent is not None:
node = node.parent
last_action = action
action = node.action
return last_action
def get_action(self, perception):
""" This is the main method for all your agents
Along with the __init__, you must at least implement this method in
all your agents to make them work properly.
This method receives a perception from the environment and returns
an action after performing the A* search with manhattan_distance as
heuristics.
"""
self.start_agent(perception,
self.problem_reference,
0,
tank_capacity=self.tank_capacity)
node = util.a_star(self.problem, self.manhattan_distance)
if not node: # Search did not find any action
people_numbers = [3, 6, 7]
grid = perception[0]
for i in range(len(grid)):
for j in range(len(grid[0])):
if grid[i][j] in people_numbers:
self.problem.pp = self.problem.ppAlcan
node = util.a_star(self.problem,
self.manhattan_distance)
if not node:
return 'STOP'
action = node.action
last_action = None
while node.parent is not None:
node = node.parent
last_action = action
action = node.action
return last_action
return 'STOP'
action = node.action
la = []
la.append(action)
last_action = None
while node.parent is not None:
node = node.parent
last_action = action
action = node.action
la.append(node.action)
return last_action
def part2():
with open('input/11.txt') as f:
original_path = f.read().strip().split(',')
goal = furthest_from_origin(original_path)
print(goal)
def heuristic(p):
x, y = p
gx, gy = goal
return math.hypot((gx - x), (gy - y))
shortest_path = a_star((0, 0), goal, moves, heuristic)
print(len(shortest_path) -
1) # remove one, becuase path includes the start state
def get_action(self, perception):
""" Receives a perception, do a search and returns an action """
self.start_agent(perception,
self.problem_reference,
tank_capacity=self.tank_capacity)
node = util.a_star(self.problem, self.heuristic)
if not node: # Search did not find any action
return 'STOP'
action = node.action
last_action = None
while node.parent is not None:
node = node.parent
last_action = action
action = node.action
return last_action
def get_action(self, perception):
"""
Copied from GetClosestPersonOrRefillAgent
Receives a perception, do a search and returns an action
"""
self.start_agent(perception,
self.problem_reference,
tank_capacity=self.tank_capacity)
node = util.a_star(self.problem, self.avg_manhattan_distance)
if not node: # Search did not find any action
return 'STOP'
action = node.action
last_action = None
while node.parent is not None:
node = node.parent
last_action = action
action = node.action
return last_action
def get_action(self, perception):
""" This is the main method for all your agents
Along with the __init__, you must at least implement this method in
all your agents to make them work properly.
This method receives a perception from the environment and returns
an action after performing the A* search with manhattan_distance as
heuristics.
"""
self.start_agent(perception, self.problem_reference,
tank_capacity=self.tank_capacity)
node = util.a_star(self.problem, self.manhattan_distance)
if not node: # Search did not find any action
return 'STOP'
action = node.action
last_action = None
while node.parent is not None:
node = node.parent
last_action = action
action = node.action
return last_action
def test():
def heuristic(p):
x, y = p
return math.hypot(2 - x, 2 - y)
print(a_star((0, 0), (2, 2), moves, heuristic))