def getFeatures(self, state, action):
# extract the grid of food and wall locations and get the ghost locations
food = state.getFood()
walls = state.getWalls()
ghosts = state.getGhostPositions()
features = CustomCounter()
features["bias"] = 1.0
# compute the location of pacman after he takes the action
x, y = state.getPacmanPosition()
# features["pacman"] = [x, y]
dx, dy = Actions.directionToVector(action)
next_x, next_y = int(x + dx), int(y + dy)
# count the number of ghosts 1-step away
features["#-of-ghosts-1-step-away"] = sum(
(next_x, next_y) in Actions.getLegalNeighbors(g, walls)
for g in ghosts)
# if there is no danger of ghosts then add the food feature
if not features["#-of-ghosts-1-step-away"] and food[next_x][next_y]:
features["eats-food"] = 1.0
dist, pos_x, pos_y = closestFood((next_x, next_y), food, walls)
if dist is not None:
# make the distance a number less than one otherwise the update
# will diverge wildly
features["closest-food"] = round(
float(dist) / (walls.width * walls.height), 5)
# features["dist"] = dist
# features["food-location"] = [pos_x, pos_y]
# features.divideAll(10.0)
return features
def getFeatures(self, state, action):
# extract the grid of food and wall locations and get the ghost locations
food = state.getFood()
walls = state.getWalls()
ghosts = state.getGhostPositions()
features = CustomCounter()
features["bias"] = 1.0
# compute the location of pacman after he takes the action
x, y = state.getPacmanPosition()
dx, dy = Actions.directionToVector(action)
next_x, next_y = int(x + dx), int(y + dy)
# count the number of ghosts 1-step away
# get position of ghost only when it is not scared
# in this way pacman may learn to eat scared ghost
features["#-of-ghosts-1-step-away"] = sum(
(next_x, next_y) in Actions.getLegalNeighbors(g_s.getPosition(), walls)
for g_s in state.getGhostStates() if not g_s.scaredTimer)
# if there is no danger of ghosts then add the food feature
if not features["#-of-ghosts-1-step-away"] and food[next_x][next_y]:
features["eats-food"] = 1.0
else:
features["eats-food"] = 0.0
dist = closestFood((next_x, next_y), food, walls)
if dist is not None:
# make the distance a number less than one otherwise the update
# will diverge wildly
features["closest-food"] = float(dist) / (walls.width * walls.height)
features.divideAll(10.0)
return features
def distance_to_the_nearest_item_from_list(pos,
items_positions,
walls,
cutout=3):
"""
returns the distance to the item from items_positions (ghosts, capsules,...)
cutout is used to stop searching when distance is getting higher than cutout value
"""
fringe = [(pos[0], pos[1], 0)]
expanded = set()
while fringe:
pos_x, pos_y, dist = fringe.pop(0)
if (pos_x, pos_y) in expanded:
continue
if dist > cutout:
continue
expanded.add((pos_x, pos_y))
# if we find a food at this location then exit
if (pos_x, pos_y) in items_positions:
return dist
# otherwise spread out from the location to its neighbours
nbrs = Actions.getLegalNeighbors((pos_x, pos_y), walls)
for nbr_x, nbr_y in nbrs:
if nbr_x == pos[0] and nbr_y == pos[
1] or nbr_x == pos_x and nbr_y == pos_y:
continue
fringe.append((nbr_x, nbr_y, dist + 1))
# no ghost found
return None
def closestFood(pos, food, walls):
fringe = [(pos[0], pos[1], 0)]
expanded = set()
while fringe:
pos_x, pos_y, dist = fringe.pop(0)
if (pos_x, pos_y) in expanded:
continue
expanded.add((pos_x, pos_y))
# if we find a food at this location then exit
if food[pos_x][pos_y]:
return dist, pos_x, pos_y
# otherwise spread out from the location to its neighbours
nbrs = Actions.getLegalNeighbors((pos_x, pos_y), walls)
for nbr_x, nbr_y in nbrs:
fringe.append((nbr_x, nbr_y, dist + 1))
# no food found
return None
def closest_cell(pos, cells, walls):
dists = {c: -1 for c in cells}
fringe = [(pos[0], pos[1], 0)]
expanded = set()
while fringe:
pos_x, pos_y, dist = fringe.pop(0)
if (pos_x, pos_y) in expanded:
continue
expanded.add((pos_x, pos_y))
if (pos_x, pos_y) in cells:
if dists[(pos_x, pos_y)] == -1:
dists[(pos_x, pos_y)] = dist
if all([v != -1 for v in dists.values()]):
break
nbrs = Actions.getLegalNeighbors((pos_x, pos_y), walls)
for nbr_x, nbr_y in nbrs:
fringe.append((nbr_x, nbr_y, dist + 1))
return min(list(dists.items()), key=lambda cd: cd[1])
def closestFood(pos, food, walls):
"""
closestFood -- this is similar to the function that we have
worked on in the search project; here its all in one place
"""
fringe = [(pos[0], pos[1], 0)]
expanded = set()
while fringe:
pos_x, pos_y, dist = fringe.pop(0)
if (pos_x, pos_y) in expanded:
continue
expanded.add((pos_x, pos_y))
# if we find a food at this location then exit
if food[pos_x][pos_y]:
return dist
# otherwise spread out from the location to its neighbours
nbrs = Actions.getLegalNeighbors((pos_x, pos_y), walls)
for nbr_x, nbr_y in nbrs:
fringe.append((nbr_x, nbr_y, dist + 1))
# no food found
return None
def getFeatures(self, state, action):
# extract the grid of food and wall locations and get the ghost locations
food = state.getFood()
walls = state.getWalls()
ghosts = state.getGhostPositions()
features = CustomCounter()
features["bias"] = 1.0
# compute the location of pacman after he takes the action
x, y = state.getPacmanPosition()
dx, dy = Actions.directionToVector(action)
next_x, next_y = int(x + dx), int(y + dy)
not_scared_ghosts_positions = [
g_s.getPosition() for g_s in state.getGhostStates()
if not g_s.scaredTimer
]
# count the number of ghosts 1-step away
# get position of ghost only when it is not scared
# in this way pacman may learn to eat scared ghost
features["#-of-ghosts-1-step-away"] = sum(
(next_x, next_y) in Actions.getLegalNeighbors(g_s, walls)
for g_s in not_scared_ghosts_positions)
# ghost_quarters = [get_quarter_from_position(ghost_position, walls)
# for ghost_position in state.getGhostPositions()]
#
# if all(ghost_quarters) == get_quarter_from_position((x, y), walls):
# features['pacman-and-ghosts-in-the-same-region'] = 1.0
# else:
# features['pacman-and-ghosts-in-the-same-region'] = 0.0
ghost_distance_limit = 3
nearest_ghost = distance_to_the_nearest_item_from_list(
(next_x, next_y), not_scared_ghosts_positions, walls, cutout=3)
if nearest_ghost is not None:
nearest_ghost = max(nearest_ghost - 1, 0)
if nearest_ghost < 1:
features['ghost-is-nearby'] = 1
else:
features['ghost-is-nearby'] = (
ghost_distance_limit -
nearest_ghost) / ghost_distance_limit
else:
features['ghost-is-nearby'] = 0
# nearest_capsule = distance_to_the_nearest_item_from_list((next_x, next_y), state.getCapsules(), walls, cutout=0)
## set 1.0 if the nearest capsule is closer than the nearest ghost
# if nearest_capsule is not None and nearest_ghost is None or \
# nearest_capsule is not None and nearest_ghost is not None and nearest_capsule < nearest_ghost:
# features["capsule-is-nearby"] = 1.0
# features["capsule-is-nearby"] = 1.0 if nearest_capsule is not None else 0.0
# if there is no danger of ghosts then add the food feature
if not features["#-of-ghosts-1-step-away"] and food[next_x][next_y]:
features["eats-food"] = 1.0
else:
features["eats-food"] = 0.0
dist = closestFood((next_x, next_y), food, walls)
if dist is not None:
# make the distance a number less than one otherwise the update
# will diverge wildly
features["closest-food"] = float(dist) / (walls.width *
walls.height)
features.divideAll(10.0)
return features
def getFeatures(self, state, action):
# extract the grid of food and wall locations and get the ghost locations
food = state.getFood()
walls = state.getWalls()
ghosts = state.getGhostPositions()
capsulesLeft = len(state.getCapsules())
scaredGhost = []
activeGhost = []
features = CustomCounter()
for ghost in state.getGhostStates():
if not ghost.scaredTimer:
activeGhost.append(ghost)
else:
#print (ghost.scaredTimer)
scaredGhost.append(ghost)
pos = state.getPacmanPosition()
def getManhattanDistances(ghosts):
return map(lambda g: manhattan_distance(pos, g.getPosition()),
ghosts)
distanceToClosestActiveGhost = distanceToClosestScaredGhost = 0
features["bias"] = 1.0
# compute the location of pacman after he takes the action
x, y = state.getPacmanPosition()
dx, dy = Actions.directionToVector(action)
next_x, next_y = int(x + dx), int(y + dy)
# count the number of ghosts 1-step away
features["#-of-ghosts-1-step-away"] = sum(
(next_x, next_y) in Actions.getLegalNeighbors(g, walls)
for g in ghosts)
# if there is no danger of ghosts then add the food feature
if not features["#-of-ghosts-1-step-away"] and food[next_x][next_y]:
features["eats-food"] = 1.0
dist = closestFood((next_x, next_y), food, walls)
if dist is not None:
# make the distance a number less than one otherwise the update
# will diverge wildly
features["closest-food"] = float(dist) / (walls.width *
walls.height)
if scaredGhost: # and not activeGhost:
distanceToClosestScaredGhost = min(
getManhattanDistances(scaredGhost))
if activeGhost:
distanceToClosestActiveGhost = min(
getManhattanDistances(activeGhost))
else:
distanceToClosestActiveGhost = 10
features["capsules"] = capsulesLeft
#features["dist-to-closest-active-ghost"] = 2*(1./distanceToClosestActiveGhost)
if distanceToClosestScaredGhost <= 8 and distanceToClosestActiveGhost >= 2: #features["#-of-ghosts-1-step-away"] >= 1:
features["#-of-ghosts-1-step-away"] = 0
features["eats-food"] = 0.0
#features["closest-food"] = 0
features.divideAll(10.0)
return features