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
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)
features.divideAll(10.0)
return features
def __init__(self,
epsilon=0.05,
gamma=0.8,
alpha=0.2,
numTraining=900,
extractor=SimpleExtractor(),
**args):
"You can initialize Q-values here..."
args['epsilon'] = epsilon
args['gamma'] = gamma
args['alpha'] = alpha
args['numTraining'] = numTraining
self.featExtractor = extractor
self.index = 0 # This is always Pacman
self.weights = CustomCounter()
self.q_values = CustomCounter()
self.lastAction = None
ReinforcementAgent.__init__(self,
epsilon=epsilon,
gamma=gamma,
alpha=alpha,
numTraining=numTraining)
"*** YOUR CODE HERE ***"
def getFeatures(self, state, action):
feats = CustomCounter()
feats[state] = 1.0
feats['x=%d' % state[0]] = 1.0
feats['y=%d' % state[0]] = 1.0
feats['action=%s' % action] = 1.0
return feats
def __init__(self, **args):
"You can initialize Q-values here..."
ReinforcementAgent.__init__(self, **args)
self.last_move = None
self.last_index = -1
self.q_values = CustomCounter()
self.best_state_action = None
def __init__(self, extractor='IdentityExtractor', **args):
self.featExtractor = MyExtractor()
# self.featExtractor = lookup(extractor, globals())()
PacmanQAgent.__init__(self, **args)
self.train = True
self.start_with_trained_weights = True
if self.train:
self.weights = CustomCounter()
if self.start_with_trained_weights:
self.weights = {'eats-food': 3.744196232507296, 'closest-food': -0.04577473069235167,
'#-of-ghosts-1-step-away': -10.027445395331362, 'bias': -6.304746841317069}
def __init__(self,
extractor='IdentityExtractor',
train=False,
optilio=False,
weights_values=[0, 0, 0, 0],
**args):
self.featExtractor = lookup(extractor, globals())()
PacmanQAgent.__init__(self, **args)
self.weights = CustomCounter()
self.weight = 0
self.optilio = optilio
self.train = train
if self.train:
for number, feature in zip(weights_values, self.weights):
self.weights[feature] = float(number)
def __init__(self, extractor='IdentityExtractor', **args):
self.featExtractor = MyExtractor()
# self.featExtractor = lookup(extractor, globals())()
PacmanQAgent.__init__(self, **args)
self.train = False
self.start_with_trained_weights = True
if self.train:
self.weights = CustomCounter()
if self.start_with_trained_weights:
# self.weights = {'capsule-is-nearby': 0.0, 'bias': -5.848186678365304, 'ghost-is-nearby': -47.767678591296, 'eats-food': 4.2052171620675125, '#-of-ghosts-1-step-away': -10.053403840432816, 'closest-food': -0.09139458036757105}
self.weights = {
'ghost-is-nearby': -19.71752071573443,
'bias': -5.853012565325543,
'eats-food': 4.2186468885699115,
'#-of-ghosts-1-step-away': -10.029721097702556,
'closest-food': -0.09146973122692789
}
def __init__(self,
epsilon=0.25,
gamma=0.8,
alpha=0.2,
numTraining=0,
extractor=IdentityExtractor(),
**args):
"You can initialize Q-values here..."
args['epsilon'] = epsilon
args['gamma'] = gamma
args['alpha'] = alpha
args['numTraining'] = numTraining
self.featExtractor = extractor
self.index = 0 # This is always Pacman
self.weights = CustomCounter()
ReinforcementAgent.__init__(self, **args)
"*** YOUR CODE HERE ***"
def __init__(self,
extractor='IdentityExtractor',
train=False,
optilio=False,
weights_values=[0, 0, 0, 0, 0],
**args):
self.featExtractor = lookup(extractor, globals())()
PacmanQAgent.__init__(self, **args)
self.weights = CustomCounter()
self.weight = 0
self.optilio = optilio
self.train = train
if (not self.train
) or self.optilio or weights_values != [0, 0, 0, 0, 0]:
for number, feature in zip(weights_values, [
"bias", "#-of-ghosts-1-step-away", "eats-food",
"closest-food", "capsules"
]):
self.weights[feature] = float(number)
if not self.optilio:
# print(self.weights)
pass
def getFeatures(self, state, action):
feats = CustomCounter()
feats[(state, action)] = 1.0
return feats
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
def __init__(self, extractor='IdentityExtractor', **args):
self.featExtractor = lookup(extractor, globals())()
PacmanQAgent.__init__(self, **args)
self.weights = CustomCounter()
