def registerInitialState(self, gameState):
"""
This method handles the initial setup of the
agent to populate useful fields (such as what team
we're on).
A distanceCalculator instance caches the maze distances
between each pair of positions, so your agents can use:
self.distancer.getDistance(p1, p2)
IMPORTANT: This method may run for at most 15 seconds.
"""
'''
Make sure you do not delete the following line. If you would like to
use Manhattan distances instead of maze distances in order to save
on initialization time, please take a look at
CaptureAgent.registerInitialState in captureAgents.py.
'''
self.start = gameState.getAgentPosition(self.index)
self.team = CaptureAgent.getTeam(self, gameState)
self.food = CaptureAgent.getFood(self, gameState)
self.foodDefending = CaptureAgent.getFoodYouAreDefending(
self, gameState)
self.opponents = CaptureAgent.getOpponents(self, gameState)
print("Start: ", self.start, "Team: ", self.team, "Food: ", self.food,
"Food Defending:", self.foodDefending, "Opponent: ",
self.opponents)
CaptureAgent.registerInitialState(self, gameState)
'''
def registerInitialState(self, gameState):
CaptureAgent.registerInitialState(self, gameState)
self.myAgents = CaptureAgent.getTeam(self, gameState)
self.opponentsAgents = CaptureAgent.getOpponents(self, gameState)
self.myFoods = CaptureAgent.getFood(self, gameState).asList()
self.opponentsFoods = CaptureAgent.getFoodYouAreDefending(
self, gameState).asList()
def getFoodCentroid(self, gameState):
"""
This function searches the friendly team's half and calculates
the geometric mean of food items.
"""
matrix = CaptureAgent.getFoodYouAreDefending(self, gameState)
#Gets all the food we are currently defending
food_row = [0] * 16
food_row = np.array(food_row)
centroid_row = 0
food_col = [0] * 16
food_col = np.array(food_col)
centroid_col = 0
num_food = 0
#Creates an array of 16 rows and columns representing our side
if (self.red):
for x in range(16):
food_col[x] = 0
for y in range(16):
#If the position on our side contains food, add to the column counter
if (matrix[x][y] == True):
food_col[x] = food_col[x] + 1
centroid_col += food_col[x] * x
for y in range(16):
food_row[y] = 0
for x in range(16):
#If the position on our side contrains foor, add to the row counter
if (matrix[x][y] == True):
num_food += 1
food_row[y] = food_row[y] + 1
centroid_row += food_row[y] * y
#Calculate the centroid of the food based on the frequency of the food in rows and columns
position = int(round(centroid_col / num_food)), int(
round(centroid_row / num_food))
else:
#The same logic, but for blue. Everything else is similar but there are offsets for the blue side.
for x in range(16):
food_col[x] = 0
for y in range(16):
if (matrix[x + 16][y] == True):
food_col[x] = food_col[x] + 1
centroid_col += food_col[x] * x
for y in range(16):
food_row[y] = 0
for x in range(16):
if (matrix[x + 16][y] == True):
num_food += 1
food_row[y] = food_row[y] + 1
centroid_row += food_row[y] * y
position = int(round(centroid_col / num_food)) + 16, int(
round(centroid_row / num_food))
return position
def registerInitialState(self, gameState): """ This method handles the initial setup of the agent to populate useful fields (such as what team we're on). """ CaptureAgent.registerInitialState(self, gameState) self.myAgents = CaptureAgent.getTeam(self, gameState) self.opAgents = CaptureAgent.getOpponents(self, gameState) self.myFoods = CaptureAgent.getFood(self, gameState).asList() self.opFoods = CaptureAgent.getFoodYouAreDefending(self, gameState).asList()
def getFoodCentroid(self, gameState):
"""
This function searches the friendly team's half and calculates
the geometric mean of food items.
"""
matrix = CaptureAgent.getFoodYouAreDefending(self, gameState)
food_row = [0] * 16
food_row = np.array(food_row)
centroid_row = 0
food_col = [0] * 16
food_col = np.array(food_col)
centroid_col = 0
num_food = 0
if (self.red):
for x in range(16):
food_col[x] = 0
for y in range(16):
if (matrix[x][y] == True):
food_col[x] = food_col[x] + 1
centroid_col += food_col[x] * x
for y in range(16):
food_row[y] = 0
for x in range(16):
if (matrix[x][y] == True):
num_food += 1
food_row[y] = food_row[y] + 1
centroid_row += food_row[y] * y
position = int(round(centroid_col / num_food)), int(
round(centroid_row / num_food))
else:
for x in range(16):
food_col[x] = 0
for y in range(16):
if (matrix[x + 16][y] == True):
food_col[x] = food_col[x] + 1
centroid_col += food_col[x] * x
for y in range(16):
food_row[y] = 0
for x in range(16):
if (matrix[x + 16][y] == True):
num_food += 1
food_row[y] = food_row[y] + 1
centroid_row += food_row[y] * y
position = int(round(centroid_col / num_food)) + 16, int(
round(centroid_row / num_food))
return position
def get_distances_to_food_factor(self, agent_position, agent_index,
enemy_team):
if (enemy_team):
food = CaptureAgent.getFoodYouAreDefending(self.root_agent_object,
self.node_state)
else:
food = CaptureAgent.getFood(self.root_agent_object,
self.node_state)
distances = []
blue_capsule = (-1, -1)
red_capsule = (-1, -1)
for c in self.node_state.data.capsules:
if c[0] > self.node_state.data.layout.width / 2:
blue_capsule = c
else:
red_capsule = c
blue_side = agent_index % 2
if blue_side:
our_capsule = red_capsule
else:
our_capsule = blue_capsule
for i in range(0, food.width):
for j in range(0, food.height):
if food.data[i][j] or our_capsule == (i, j):
distance = CaptureAgent.getMazeDistance(
self.root_agent_object, agent_position, (i, j))
distances.append(distance)
if (len(distances) == 0): return 1
#distance_to_food_factor = sum(distances) / float(len(distances))
distance_to_food_factor = min(distances)
if (distance_to_food_factor == 0):
distance_to_food_factor = 1
distance_to_food_factor = 1 / distance_to_food_factor
distance_to_food_factor = distance_to_food_factor * 1
return distance_to_food_factor
def registerInitialState(self, gameState):
CaptureAgent.registerInitialState(self, gameState)
self.init = gameState.getAgentState(self.index).getPosition()
self.epsilon = 0.0#exploration prob
self.alpha = 0.1 #learning rate
self.discountRate = 0.8
self.dangeFood = []
self.mode = 1
self.weights1 = {'closest-food': 2.35219445732408, 'bias': 2.579502234147277, 'closest-capsule': 2.473714473123398}
self.weights2 = {'closest-food': 1.02910769618005556, 'bias': -6.112936837778204, 'closest-ghosts': -10.11587156566253, 'closest-capsule': 1.257363246901937, 'num-of-walls': -10.4903928122119086, 'time-of-scared-ghosts': 1.6265815562445105, 'reverse': -2.732599631268455}
self.weights3 = {'bias': -0.1619191782335229, 'closest-ghosts': -18.645334316865307, 'num-of-walls': -10.45335435502801, 'distance-back-to-home': 2.0996715469522775, 'time-of-scared-ghosts': 0.7966612961334337, 'reverse': -2.732599631268455,'closest-capsule': 4.523232232232323}
self.weights4 = {'bias': 6.802602309336149, 'distance-back-to-home': 12.7541385540534}
self.finish = False
self.myAgents = CaptureAgent.getTeam(self, gameState)
self.opAgents = CaptureAgent.getOpponents(self, gameState)
self.myFoods = CaptureAgent.getFood(self, gameState).asList()
self.opFoods = CaptureAgent.getFoodYouAreDefending(self, gameState).asList()
self.lostFoods = []
self.gamescore = 0
self.midWidth = gameState.data.layout.width / 2
self.height = gameState.data.layout.height
self.width = gameState.data.layout.width
self.midHeight = gameState.data.layout.height / 2
