class GreedyBustersAgent(BustersAgent):
"An agent that charges the closest ghost."
def registerInitialState(self, gameState):
"Pre-computes the distance between every two points."
BustersAgent.registerInitialState(self, gameState)
self.distancer = Distancer(gameState.data.layout, False)
def chooseAction(self, gameState):
"""
First computes the most likely position of each ghost that has
not yet been captured, then chooses an action that brings
Pacman closest to the closest ghost (according to mazeDistance!).
"""
pacmanPosition = gameState.getPacmanPosition()
legal = [a for a in gameState.getLegalPacmanActions()]
livingGhosts = gameState.getLivingGhosts()
livingGhostPositionDistributions = \
[beliefs for i, beliefs in enumerate(self.ghostBeliefs)
if livingGhosts[i+1]]
distances = inference.DiscreteDistribution()
for pos in [x.argMax() for x in livingGhostPositionDistributions]:
distances[pos] = -self.distancer.getDistance(pacmanPosition, pos)
targetPos = distances.argMax()
distances = inference.DiscreteDistribution()
for action in legal:
successorPosition = Actions.getSuccessor(pacmanPosition, action)
distances[action] = -self.distancer.getDistance(targetPos, \
successorPosition)
return distances.argMax()
class GreedyBustersAgent(BustersAgent):
"An agent that charges the closest ghost."
def registerInitialState(self, gameState):
"Pre-computes the distance between every two points."
BustersAgent.registerInitialState(self, gameState)
self.distancer = Distancer(gameState.data.layout, False)
def chooseAction(self, gameState):
"""
First computes the most likely position of each ghost that has
not yet been captured, then chooses an action that brings
Pacman closest to the closest ghost (according to mazeDistance!).
"""
pacmanPosition = gameState.getPacmanPosition()
legal = [a for a in gameState.getLegalPacmanActions()]
livingGhosts = gameState.getLivingGhosts()
livingGhostPositionDistributions = \
[beliefs for i, beliefs in enumerate(self.ghostBeliefs)
if livingGhosts[i+1]]
"*** YOUR CODE HERE ***"
maxBeliefPos = [max(l, key = l.get) for l in livingGhostPositionDistributions]
closestGhost = min(maxBeliefPos, key = lambda pos: self.distancer.getDistance(pacmanPosition, pos))
successorPositions = [(Actions.getSuccessor(pacmanPosition, action), action) for action in legal]
closestAction = min([(self.distancer.getDistance(closestGhost, pos), action) for pos, action in successorPositions])[1]
return closestAction
class GreedyBustersAgent(BustersAgent):
"An agent that charges the closest ghost."
def registerInitialState(self, gameState):
"Pre-computes the distance between every two points."
BustersAgent.registerInitialState(self, gameState)
self.distancer = Distancer(gameState.data.layout, False)
def chooseAction(self, gameState):
"""
First computes the most likely position of each ghost that has
not yet been captured, then chooses an action that brings
Pacman closer to the closest ghost (according to mazeDistance!).
To find the mazeDistance between any two positions, use:
self.distancer.getDistance(pos1, pos2)
To find the successor position of a position after an action:
successorPosition = Actions.getSuccessor(position, action)
livingGhostPositionDistributions, defined below, is a list of
util.Counter objects equal to the position belief
distributions for each of the ghosts that are still alive. It
is defined based on (these are implementation details about
which you need not be concerned):
1) gameState.getLivingGhosts(), a list of booleans, one for each
agent, indicating whether or not the agent is alive. Note
that pacman is always agent 0, so the ghosts are agents 1,
onwards (just as before).
2) self.ghostBeliefs, the list of belief distributions for each
of the ghosts (including ghosts that are not alive). The
indices into this list should be 1 less than indices into the
gameState.getLivingGhosts() list.
"""
pacmanPosition = gameState.getPacmanPosition()
legal = [a for a in gameState.getLegalPacmanActions()]
livingGhosts = gameState.getLivingGhosts()
livingGhostPositionDistributions = \
[beliefs for i, beliefs in enumerate(self.ghostBeliefs)
if livingGhosts[i+1]]
closestPos, minDistance = None, 100000
for livingGhostPositionDistribution in livingGhostPositionDistributions:
likelyPosition = self.findLikelyPosition(livingGhostPositionDistribution)
distance = self.distancer.getDistance(pacmanPosition,likelyPosition)
if distance < minDistance:
minDistance, closestPos = distance, likelyPosition
for action in legal:
successorPosition = Actions.getSuccessor(pacmanPosition, action)
if self.distancer.getDistance(pacmanPosition,closestPos) > \
self.distancer.getDistance(successorPosition,closestPos):
return action
def findLikelyPosition(self, livingGhostPositionDistribution):
max_prob, max_pos = 0, None
for pos, prob in livingGhostPositionDistribution.items():
if prob > max_prob:
max_prob, max_pos = prob, pos
return max_pos
class GreedyBustersAgent(BustersAgent):
"An agent that charges the closest ghost."
def registerInitialState(self, gameState):
"Pre-computes the distance between every two points."
BustersAgent.registerInitialState(self, gameState)
self.distancer = Distancer(gameState.data.layout, False)
def chooseAction(self, gameState):
"""
First computes the most likely position of each ghost that
has not yet been captured, then chooses an action that brings
Pacman closer to the closest ghost (in maze distance!).
To find the maze distance between any two positions, use:
self.distancer.getDistance(pos1, pos2)
To find the successor position of a position after an action:
successorPosition = Actions.getSuccessor(position, action)
livingGhostPositionDistributions, defined below, is a list of
util.Counter objects equal to the position belief distributions
for each of the ghosts that are still alive. It is defined based
on (these are implementation details about which you need not be
concerned):
1) gameState.getLivingGhosts(), a list of booleans, one for each
agent, indicating whether or not the agent is alive. Note
that pacman is always agent 0, so the ghosts are agents 1,
onwards (just as before).
2) self.ghostBeliefs, the list of belief distributions for each
of the ghosts (including ghosts that are not alive). The
indices into this list should be 1 less than indices into the
gameState.getLivingGhosts() list.
"""
pacmanPosition = gameState.getPacmanPosition()
legal = [a for a in gameState.getLegalPacmanActions()]
livingGhosts = gameState.getLivingGhosts()
livingGhostPositionDistributions = [beliefs for i,beliefs
in enumerate(self.ghostBeliefs)
if livingGhosts[i+1]]
"*** YOUR CODE HERE ***"
closestDist = None
closestGhostPos = None
for ghost, dist in enumerate(livingGhostPositionDistributions):
positions = dist.keys()
positions.sort(cmp=lambda a, b: cmp(dist[a], dist[b]))
pos = positions[-1]
distance = self.distancer.getDistance(pacmanPosition, pos)
if distance < closestDist or closestDist is None:
closestGhostPos = pos
closestDist = distance
actionDistances = [(a, self.distancer.getDistance(Actions.getSuccessor(pacmanPosition, a), closestGhostPos)) for a in legal]
bestAction = min(actionDistances, key=lambda ad: ad[1])[0]
return bestAction
class GreedyBustersAgent(BustersAgent):
"An agent that charges the closest ghost."
def registerInitialState(self, gameState):
"Pre-computes the distance between every two points."
BustersAgent.registerInitialState(self, gameState)
self.distancer = Distancer(gameState.data.layout, False)
def chooseAction(self, gameState):
"""
First computes the most likely position of each ghost that has
not yet been captured, then chooses an action that brings
Pacman closest to the closest ghost (according to mazeDistance!).
"""
pacmanPosition = gameState.getPacmanPosition()
legal = [a for a in gameState.getLegalPacmanActions()]
livingGhosts = gameState.getLivingGhosts()
livingGhostPositionDistributions = \
[beliefs for i, beliefs in enumerate(self.ghostBeliefs)
if livingGhosts[i+1]]
"*** YOUR CODE HERE ***"
maxPositions = [belief.argMax() for belief in livingGhostPositionDistributions]
dist = [self.distancer.getDistance(pos,pacmanPosition) for pos in maxPositions]
minPos = maxPositions[dist.index(min(dist))]
successorDistance = [self.distancer.getDistance(minPos,Actions.getSuccessor(pacmanPosition, action)) for action in legal]
return legal[successorDistance.index(min(successorDistance))]
class GreedyBustersAgent(BustersAgent):
"An agent that charges the closest ghost."
def registerInitialState(self, gameState):
"Pre-computes the distance between every two points."
BustersAgent.registerInitialState(self, gameState)
self.distancer = Distancer(gameState.data.layout, False)
def chooseAction(self, gameState):
"""
First computes the most likely position of each ghost that has
not yet been captured, then chooses an action that brings
Pacman closer to the closest ghost (according to mazeDistance!).
"""
pacmanPosition = gameState.getPacmanPosition()
legal = [a for a in gameState.getLegalPacmanActions()]
livingGhosts = gameState.getLivingGhosts()
livingGhostPositionDistributions = \
[beliefs for i, beliefs in enumerate(self.ghostBeliefs)
if livingGhosts[i+1]]
"*** YOUR CODE HERE ***"
ghost_dist = {}
for ghost_distribution in livingGhostPositionDistributions:
most_likely_spot = max(ghost_distribution, key=ghost_distribution.get)
ghost_dist[most_likely_spot] = self.distancer.getDistance(pacmanPosition, most_likely_spot)
closest_ghost = min(ghost_dist, key=ghost_dist.get)
possible_actions = {}
for action in gameState.getLegalActions():
next_pacman_spot = Actions.getSuccessor(pacmanPosition, action)
possible_actions[action] = self.distancer.getDistance(next_pacman_spot, closest_ghost)
best_action = min(possible_actions, key=possible_actions.get)
return best_action
class GreedyBustersAgent(BustersAgent):
"An agent that charges the closest ghost."
def registerInitialState(self, gameState):
"Pre-computes the distance between every two points."
BustersAgent.registerInitialState(self, gameState)
self.distancer = Distancer(gameState.data.layout, False)
def chooseAction(self, gameState):
"""
First computes the most likely position of each ghost that
has not yet been captured, then chooses an action that brings
Pacman closer to the closest ghost (in maze distance!).
To find the maze distance between any two positions, use:
self.distancer.getDistance(pos1, pos2)
To find the successor position of a position after an action:
successorPosition = Actions.getSuccessor(position, action)
livingGhostPositionDistributions, defined below, is a list of
util.Counter objects equal to the position belief distributions
for each of the ghosts that are still alive. It is defined based
on (these are implementation details about which you need not be
concerned):
1) gameState.getLivingGhosts(), a list of booleans, one for each
agent, indicating whether or not the agent is alive. Note
that pacman is always agent 0, so the ghosts are agents 1,
onwards (just as before).
2) self.ghostBeliefs, the list of belief distributions for each
of the ghosts (including ghosts that are not alive). The
indices into this list should be 1 less than indices into the
gameState.getLivingGhosts() list.
"""
pacmanPosition = gameState.getPacmanPosition()
legal = [a for a in gameState.getLegalPacmanActions()]
livingGhosts = gameState.getLivingGhosts()
livingGhostPositionDistributions = [beliefs for i,beliefs
in enumerate(self.ghostBeliefs)
if livingGhosts[i+1]]
"*** YOUR CODE HERE ***"
closest=None
min = float("inf")
for x in livingGhostPositionDistributions:
mostProb = x.argMax()
dist = self.distancer.getDistance(pacmanPosition,mostProb)
if dist<min:
min=dist
closest=mostProb
action=None
min = float("inf")
for act in legal:
dist=self.distancer.getDistance(Actions.getSuccessor(pacmanPosition,act),mostProb)
if dist<min:
min=dist
action=act
return action
class GreedyBustersAgent(BustersAgent):
"An agent that charges the closest ghost."
def registerInitialState(self, gameState):
"Pre-computes the distance between every two points."
BustersAgent.registerInitialState(self, gameState)
self.distancer = Distancer(gameState.data.layout, False)
def mydistance(x, y, pacmanPosition):
ghostLocation = (x, y)
return self.distancer.getDistance(pacmanPosition, ghostLocation)
def chooseAction(self, gameState):
"""
First computes the most likely position of each ghost that
has not yet been captured, then chooses an action that brings
Pacman closer to the closest ghost (in maze distance!).
To find the maze distance between any two positions, use:
self.distancer.getDistance(pos1, pos2)
To find the successor position of a position after an action:
successorPosition = Actions.getSuccessor(position, action)
livingGhostPositionDistributions, defined below, is a list of
util.Counter objects equal to the position belief distributions
for each of the ghosts that are still alive. It is defined based
on (these are implementation details about which you need not be
concerned):
1) gameState.getLivingGhosts(), a list of booleans, one for each
agent, indicating whether or not the agent is alive. Note
that pacman is always agent 0, so the ghosts are agents 1,
onwards (just as before).
2) self.ghostBeliefs, the list of belief distributions for each
of the ghosts (including ghosts that are not alive). The
indices into this list should be 1 less than indices into the
gameState.getLivingGhosts() list.
"""
pacmanPosition = gameState.getPacmanPosition()
legal = [a for a in gameState.getLegalPacmanActions()]
livingGhosts = gameState.getLivingGhosts()
livingGhostPositionDistributions = [
beliefs for i, beliefs in enumerate(self.ghostBeliefs) if livingGhosts[i + 1]
]
ghostPositions = [max(distribution, key=distribution.get) for distribution in livingGhostPositionDistributions]
print "ghostPositions", ghostPositions
closestGhostPosition = min(ghostPositions, key=lambda x: self.distancer.getDistance(pacmanPosition, x))
print "closestGhostPosition", closestGhostPosition
successorPositions = [Actions.getSuccessor(pacmanPosition, action) for action in legal]
closestSuccessorPosition = min(
successorPositions, key=lambda x: self.distancer.getDistance(closestGhostPosition, x)
)
move = legal[successorPositions.index(closestSuccessorPosition)]
return move
class GreedyBustersAgent(BustersAgent):
"An agent that charges the closest ghost."
def registerInitialState(self, gameState):
"Pre-computes the distance between every two points."
BustersAgent.registerInitialState(self, gameState)
self.distancer = Distancer(gameState.data.layout, False)
def chooseAction(self, gameState):
"""
First computes the most likely position of each ghost that has
not yet been captured, then chooses an action that brings
Pacman closer to the closest ghost (according to mazeDistance!).
To find the mazeDistance between any two positions, use:
self.distancer.getDistance(pos1, pos2)
To find the successor position of a position after an action:
successorPosition = Actions.getSuccessor(position, action)
livingGhostPositionDistributions, defined below, is a list of
util.Counter objects equal to the position belief
distributions for each of the ghosts that are still alive. It
is defined based on (these are implementation details about
which you need not be concerned):
1) gameState.getLivingGhosts(), a list of booleans, one for each
agent, indicating whether or not the agent is alive. Note
that pacman is always agent 0, so the ghosts are agents 1,
onwards (just as before).
2) self.ghostBeliefs, the list of belief distributions for each
of the ghosts (including ghosts that are not alive). The
indices into this list should be 1 less than indices into the
gameState.getLivingGhosts() list.
"""
pacmanPosition = gameState.getPacmanPosition()
legal = [a for a in gameState.getLegalPacmanActions()]
livingGhosts = gameState.getLivingGhosts()
livingGhostPositionDistributions = \
[beliefs for i, beliefs in enumerate(self.ghostBeliefs)
if livingGhosts[i+1]]
"*** YOUR CODE HERE ***"
# get max position for every ghost
def getMaxDictKey(d):
maxKeys = [k for k in d.keys() if d[k] == max(d.values())]
return maxKeys[0]
maxPos = [getMaxDictKey(k) for k in livingGhostPositionDistributions]
distances = map(lambda x: self.distancer.getDistance(x, pacmanPosition), maxPos)
minDist = min(distances)
goalPos = maxPos[distances.index(minDist)]
for action in legal:
if self.distancer.getDistance(Actions.getSuccessor(pacmanPosition, action), goalPos) < minDist:
return action
# should not end here!
return legal[0]
class GreedyBustersAgent(BustersAgent):
"An agent that charges the closest ghost."
def registerInitialState(self, gameState):
"Pre-computes the distance between every two points."
BustersAgent.registerInitialState(self, gameState)
self.distancer = Distancer(gameState.data.layout, False)
def chooseAction(self, gameState):
"""
First computes the most likely position of each ghost that has
not yet been captured, then chooses an action that brings
Pacman closer to the closest ghost (according to mazeDistance!).
To find the mazeDistance between any two positions, use:
self.distancer.getDistance(pos1, pos2)
To find the successor position of a position after an action:
successorPosition = Actions.getSuccessor(position, action)
livingGhostPositionDistributions, defined below, is a list of
util.Counter objects equal to the position belief
distributions for each of the ghosts that are still alive. It
is defined based on (these are implementation details about
which you need not be concerned):
1) gameState.getLivingGhosts(), a list of booleans, one for each
agent, indicating whether or not the agent is alive. Note
that pacman is always agent 0, so the ghosts are agents 1,
onwards (just as before).
2) self.ghostBeliefs, the list of belief distributions for each
of the ghosts (including ghosts that are not alive). The
indices into this list should be 1 less than indices into the
gameState.getLivingGhosts() list.
"""
pacmanPosition = gameState.getPacmanPosition()
legal = [a for a in gameState.getLegalPacmanActions()]
livingGhosts = gameState.getLivingGhosts()
livingGhostPositionDistributions = \
[beliefs for i, beliefs in enumerate(self.ghostBeliefs)
if livingGhosts[i+1]]
"*** YOUR CODE HERE ***"
mostPossiblePosition = lambda ghostPosDist: max(ghostPosDist.items(), key=lambda x: x[1])[0]
mostPossiblePositions = map(mostPossiblePosition, livingGhostPositionDistributions)
distToPacman = lambda x: self.distancer.getDistance(pacmanPosition, x)
closestGhostPos = min(mostPossiblePositions, key=distToPacman)
minDist = self.distancer.getDistance(pacmanPosition, closestGhostPos)
delta_pos = set()
for action in legal:
successorPosition = Actions.getSuccessor(pacmanPosition, action)
delta = self.distancer.getDistance(closestGhostPos, successorPosition)-minDist # the more negative the better
delta_pos.add((delta, action))
return min(delta_pos)[1]
# util.raiseNotDefined()
def observe(self, gameState, selfIndex, agentIndex):
noisyAgentDistance = gameState.getAgentDistances()[agentIndex]
distribution = Counter()
pacmanPosition = gameState.getAgentState(selfIndex).getPosition()
distancer = Distancer(gameState.data.layout)
for x in xrange(gameState.data.layout.width):
for y in xrange(gameState.data.layout.height):
if not gameState.hasWall(x, y):
position = (x, y)
trueDistance = distancer.getDistance(position, pacmanPosition)
distribution[position] = gameState.getDistanceProb(trueDistance, noisyAgentDistance) * self.distributions[agentIndex][position]
distribution.normalize()
self.distributions[agentIndex] = distribution
class GreedyBustersAgent(BustersAgent):
"An agent that charges the closest ghost."
def registerInitialState(self, gameState):
"Pre-computes the distance between every two points."
BustersAgent.registerInitialState(self, gameState)
self.distancer = Distancer(gameState.data.layout, False)
def chooseAction(self, gameState):
"""
First computes the most likely position of each ghost that has
not yet been captured, then chooses an action that brings
Pacman closer to the closest ghost (according to mazeDistance!).
To find the mazeDistance between any two positions, use:
self.distancer.getDistance(pos1, pos2)
To find the successor position of a position after an action:
successorPosition = Actions.getSuccessor(position, action)
livingGhostPositionDistributions, defined below, is a list of
util.Counter objects equal to the position belief
distributions for each of the ghosts that are still alive. It
is defined based on (these are implementation details about
which you need not be concerned):
1) gameState.getLivingGhosts(), a list of booleans, one for each
agent, indicating whether or not the agent is alive. Note
that pacman is always agent 0, so the ghosts are agents 1,
onwards (just as before).
2) self.ghostBeliefs, the list of belief distributions for each
of the ghosts (including ghosts that are not alive). The
indices into this list should be 1 less than indices into the
gameState.getLivingGhosts() list.
"""
pacmanPosition = gameState.getPacmanPosition()
legal = [a for a in gameState.getLegalPacmanActions()]
livingGhosts = gameState.getLivingGhosts()
livingGhostPositionDistributions = \
[beliefs for i, beliefs in enumerate(self.ghostBeliefs)
if livingGhosts[i+1]]
gohstPosition = [dist.argMax() for dist in livingGhostPositionDistributions]
gohstDistance = [self.distancer.getDistance(pacmanPosition, pos) for pos in gohstPosition]
closestGhostIndex = gohstDistance.index(min(gohstDistance))
allPacPotentialPos = [Actions.getSuccessor(pacmanPosition, action) for action in legal]
distanceToClosestCostAfterAllActions = \
[self.distancer.getDistance(newPacPost, gohstPosition[closestGhostIndex])\
for newPacPost in allPacPotentialPos]
return legal[distanceToClosestCostAfterAllActions.index(min(distanceToClosestCostAfterAllActions))]
class GreedyBustersAgent(BustersAgent):
"An agent that charges the closest ghost."
def registerInitialState(self, gameState):
"Pre-computes the distance between every two points."
BustersAgent.registerInitialState(self, gameState)
self.distancer = Distancer(gameState.data.layout, False)
def chooseAction(self, gameState):
"""
First computes the most likely position of each ghost that
has not yet been captured, then chooses an action that brings
Pacman closer to the closest ghost (in maze distance!).
To find the maze distance between any two positions, use:
self.distancer.getDistance(pos1, pos2)
To find the successor position of a position after an action:
successorPosition = Actions.getSuccessor(position, action)
livingGhostPositionDistributions, defined below, is a list of
util.Counter objects equal to the position belief distributions
for each of the ghosts that are still alive. It is defined based
on (these are implementation details about which you need not be
concerned):
1) gameState.getLivingGhosts(), a list of booleans, one for each
agent, indicating whether or not the agent is alive. Note
that pacman is always agent 0, so the ghosts are agents 1,
onwards (just as before).
2) self.ghostBeliefs, the list of belief distributions for each
of the ghosts (including ghosts that are not alive). The
indices into this list should be 1 less than indices into the
gameState.getLivingGhosts() list.
"""
pacman = gameState.getPacmanPosition()
legal = [a for a in gameState.getLegalPacmanActions() if a != Directions.STOP]
ghosts = gameState.getLivingGhosts()
distributions = [b for i, b in enumerate(self.ghostBeliefs) if ghosts[i + 1]]
successor = [(Actions.getSuccessor(pacman, a), a) for a in legal]
positions = [max(d.items(), key=lambda x:x[1])[0] for d in distributions]
distances = [(self.distancer.getDistance(pacman, d), d) for d in positions]
choice = min(distances)[1] # min distance between current and any ghost
actions = [(self.distancer.getDistance(choice, s[0]), s[1]) for s in successor]
action = min(actions)[1] # the action that gets us closer to the close ghost
return action
class GreedyBustersAgent(BustersAgent):
"An agent that charges the closest ghost."
def registerInitialState(self, gameState):
"Pre-computes the distance between every two points."
BustersAgent.registerInitialState(self, gameState)
self.distancer = Distancer(gameState.data.layout, False)
def chooseAction(self, gameState):
"""
First computes the most likely position of each ghost that
has not yet been captured, then chooses an action that brings
Pacman closer to the closest ghost (in maze distance!).
To find the maze distance between any two positions, use:
self.distancer.getDistance(pos1, pos2)
To find the successor position of a position after an action:
successorPosition = Actions.getSuccessor(position, action)
To get a list of booleans, one for each agent, indicating whether
or not the agent is alive, use gameState.getLivingGhosts()
Note that pacman is always agent 0, so the ghosts are agents 1,
onwards (just as before).
You may remove Directions.STOP from the list of available actions.
"""
mostLikelyGhostPositions = util.Counter();
for i in range(0,len(gameState.getLivingGhosts())):
if (gameState.getLivingGhosts()[i]):
mostLikelyGhostPositions[i] = self.inferenceModules[i-1].getBeliefDistribution().argMax()
minDist = 100000000
minIndex = 1
pacmanPosition = gameState.getPacmanPosition()
for ghostIndex in mostLikelyGhostPositions:
dist = self.distancer.getDistance(pacmanPosition,mostLikelyGhostPositions[ghostIndex])
if (min(dist,minDist) != minDist):
minDist = min(dist,minDist);
minIndex = ghostIndex;
legal = [a for a in gameState.getLegalPacmanActions() if a != Directions.STOP]
legalMoves = util.Counter();
minGhostPos = mostLikelyGhostPositions[minIndex]
for action in legal:
legalMoves[action] = -1*self.distancer.getDistance(minGhostPos,Actions.getSuccessor(pacmanPosition,action))
return legalMoves.argMax()
class GreedyBustersAgent(BustersAgent):
"An agent that charges the closest ghost."
def registerInitialState(self, gameState):
"Pre-computes the distance between every two points."
BustersAgent.registerInitialState(self, gameState)
self.distancer = Distancer(gameState.data.layout, False)
def chooseAction(self, gameState):
"""
First computes the most likely position of each ghost that
has not yet been captured, then chooses an action that brings
Pacman closer to the closest ghost (in maze distance!).
To find the maze distance between any two positions, use:
self.distancer.getDistance(pos1, pos2)
To find the successor position of a position after an action:
successorPosition = Actions.getSuccessor(position, action)
livingGhostPositionDistributions, defined below, is a list of
util.Counter objects equal to the position belief distributions
for each of the ghosts that are still alive. It is defined based
on (these are implementation details about which you need not be
concerned):
1) gameState.getLivingGhosts(), a list of booleans, one for each
agent, indicating whether or not the agent is alive. Note
that pacman is always agent 0, so the ghosts are agents 1,
onwards (just as before).
2) self.ghostBeliefs, the list of belief distributions for each
of the ghosts (including ghosts that are not alive). The
indices into this list should be 1 less than indices into the
gameState.getLivingGhosts() list.
You may remove Directions.STOP from the list of available actions.
"""
pacmanPosition = gameState.getPacmanPosition()
legal = [a for a in gameState.getLegalPacmanActions() if a != Directions.STOP]
livingGhosts = gameState.getLivingGhosts()
livingGhostPositionDistributions = [beliefs for i,beliefs
in enumerate(self.ghostBeliefs)
if livingGhosts[i+1]]
"*** YOUR CODE HERE ***"
minDist = 99999999
action = 'Stop'
for la in legal:
if la == 'North': newPacPos = (pacmanPosition[0],pacmanPosition[1]+1)
elif la == 'South': newPacPos = (pacmanPosition[0],pacmanPosition[1]-1)
elif la == 'West': newPacPos = (pacmanPosition[0]-1,pacmanPosition[1])
elif la == 'East': newPacPos = (pacmanPosition[0]+1,pacmanPosition[1])
else: print "TESTE"
for i in range(0,len(livingGhostPositionDistributions)):
ghostPos = livingGhostPositionDistributions[i].argMax()
minDist, action = min((minDist,action), (self.distancer.getDistance(newPacPos,ghostPos),la))
return action
class GreedyBustersAgent(BustersAgent):
"An agent that charges the closest ghost."
def registerInitialState(self, gameState):
"Pre-computes the distance between every two points."
BustersAgent.registerInitialState(self, gameState)
self.distancer = Distancer(gameState.data.layout, False)
def chooseAction(self, gameState):
"""
First computes the most likely position of each ghost that has
not yet been captured, then chooses an action that brings
Pacman closer to the closest ghost (according to mazeDistance!).
To find the mazeDistance between any two positions, use:
self.distancer.getDistance(pos1, pos2)
To find the successor position of a position after an action:
successorPosition = Actions.getSuccessor(position, action)
livingGhostPositionDistributions, defined below, is a list of
util.Counter objects equal to the position belief
distributions for each of the ghosts that are still alive. It
is defined based on (these are implementation details about
which you need not be concerned):
1) gameState.getLivingGhosts(), a list of booleans, one for each
agent, indicating whether or not the agent is alive. Note
that pacman is always agent 0, so the ghosts are agents 1,
onwards (just as before).
2) self.ghostBeliefs, the list of belief distributions for each
of the ghosts (including ghosts that are not alive). The
indices into this list should be 1 less than indices into the
gameState.getLivingGhosts() list.
"""
pacmanPosition = gameState.getPacmanPosition()
legal = [a for a in gameState.getLegalPacmanActions()]
livingGhosts = gameState.getLivingGhosts()
livingGhostPositionDistributions = \
[beliefs for i, beliefs in enumerate(self.ghostBeliefs)
if livingGhosts[i+1]]
# Get the position of the most likely ghost
max_prob_pos = []
for ghost_dist in livingGhostPositionDistributions:
best_pos = max(ghost_dist, key = ghost_dist.get)
max_prob_pos.append((best_pos,ghost_dist[best_pos]))
best_pos = max(max_prob_pos, key=lambda x : x[1])[0]
# Find the best move
best_dist = float("inf")
best_move = None
for move in legal:
dist = self.distancer.getDistance(best_pos, Actions.getSuccessor(pacmanPosition, move))
if dist < best_dist or not best_move:
best_move = move
best_dist = dist
return best_move
class GreedyBustersAgent(BustersAgent):
"An agent that charges the closest ghost."
def registerInitialState(self, gameState):
"Pre-computes the distance between every two points."
BustersAgent.registerInitialState(self, gameState)
self.distancer = Distancer(gameState.data.layout, False)
def chooseAction(self, gameState):
"""
First computes the most likely position of each ghost that has
not yet been captured, then chooses an action that brings
Pacman closer to the closest ghost (according to mazeDistance!).
"""
pacmanPosition = gameState.getPacmanPosition()
legal = [a for a in gameState.getLegalPacmanActions()]
livingGhosts = gameState.getLivingGhosts()
livingGhostPositionDistributions = \
[beliefs for i, beliefs in enumerate(self.ghostBeliefs)
if livingGhosts[i+1]]
"*** YOUR CODE HERE ***"
#initialize them to worst value possible
closestDis, minMazeDis = float('inf'), float('inf')
closestPos = None
theAction = None
#find the closest ghost
for oneSetDistri in livingGhostPositionDistributions:
biggestDistriPos = oneSetDistri.argMax()
mazeDis = self.distancer.getDistance(pacmanPosition, biggestDistriPos)
if (closestDis > mazeDis):
closestDis = mazeDis
closestPos = biggestDistriPos
#greedy approach: always seek for action with closest distance to get to the ghost
for action in legal:
successorPosition = Actions.getSuccessor(pacmanPosition, action)
mazeDis = self.distancer.getDistance(successorPosition, closestPos)
if (minMazeDis > mazeDis):
minMazeDis = mazeDis
theAction = action
return theAction
class GreedyBustersAgent(BustersAgent):
"An agent that charges the closest ghost."
def registerInitialState(self, gameState):
"Pre-computes the distance between every two points."
BustersAgent.registerInitialState(self, gameState)
self.distancer = Distancer(gameState.data.layout, False)
def chooseAction(self, gameState):
"""
First computes the most likely position of each ghost that has
not yet been captured, then chooses an action that brings
Pacman closer to the closest ghost (according to mazeDistance!).
To find the mazeDistance between any two positions, use:
self.distancer.getDistance(pos1, pos2)
To find the successor position of a position after an action:
successorPosition = Actions.getSuccessor(position, action)
livingGhostPositionDistributions, defined below, is a list of
util.Counter objects equal to the position belief
distributions for each of the ghosts that are still alive. It
is defined based on (these are implementation details about
which you need not be concerned):
1) gameState.getLivingGhosts(), a list of booleans, one for each
agent, indicating whether or not the agent is alive. Note
that pacman is always agent 0, so the ghosts are agents 1,
onwards (just as before).
2) self.ghostBeliefs, the list of belief distributions for each
of the ghosts (including ghosts that are not alive). The
indices into this list should be 1 less than indices into the
gameState.getLivingGhosts() list.
"""
pacmanPosition = gameState.getPacmanPosition()
legal = [a for a in gameState.getLegalPacmanActions()]
livingGhosts = gameState.getLivingGhosts()
livingGhostPositionDistributions = \
[beliefs for i, beliefs in enumerate(self.ghostBeliefs)
if livingGhosts[i+1]]
"*** YOUR CODE HERE ***"
m_dist = lambda pos1, pos2 : self.distancer.getDistance(pos1, pos2)
closest = None # closest ghost position
best_action = None # best action position
for distrib in livingGhostPositionDistributions:
mostLikelyPos = max(distrib.items(), \
key=lambda item : item[1])[0]
dist = m_dist(pacmanPosition, mostLikelyPos)
if closest == None or dist < closest[1]:
closest = (mostLikelyPos, dist)
for action in legal:
succ_pos = Actions.getSuccessor(pacmanPosition, action)
dist = m_dist(succ_pos, closest[0])
if best_action == None or dist < best_action[2]:
best_action = (action, succ_pos, dist)
return best_action[0]
def registerInitialState(self, gameState):
"Initializes beliefs and inference modules"
import __main__
self.display = __main__._display
self.distancer = Distancer(gameState.data.layout, False)
for inference in self.inferenceModules: inference.initialize(gameState)
self.ghostBeliefs = [inf.getBeliefDistribution() for inf in self.inferenceModules]
self.firstMove = True
class GreedyBustersAgent(BustersAgent):
"An agent that charges the closest ghost."
def registerInitialState(self, gameState):
"Pre-computes the distance between every two points."
BustersAgent.registerInitialState(self, gameState)
self.distancer = Distancer(gameState.data.layout, False)
def chooseAction(self, gameState):
"""
First computes the most likely position of each ghost that
has not yet been captured, then chooses an action that brings
Pacman closer to the closest ghost (in maze distance!).
To find the maze distance between any two positions, use:
self.distancer.getDistance(pos1, pos2)
To find the successor position of a position after an action:
successorPosition = Actions.getSuccessor(position, action)
livingGhostPositionDistributions, defined below, is a list of
util.Counter objects equal to the position belief distributions
for each of the ghosts that are still alive. It is defined based
on (these are implementation details about which you need not be
concerned):
1) gameState.getLivingGhosts(), a list of booleans, one for each
agent, indicating whether or not the agent is alive. Note
that pacman is always agent 0, so the ghosts are agents 1,
onwards (just as before).
2) self.ghostBeliefs, the list of belief distributions for each
of the ghosts (including ghosts that are not alive). The
indices into this list should be 1 less than indices into the
gameState.getLivingGhosts() list.
"""
pacmanPosition = gameState.getPacmanPosition()
legal = [a for a in gameState.getLegalPacmanActions()]
livingGhosts = gameState.getLivingGhosts()
livingGhostPositionDistributions = [beliefs for i,beliefs
in enumerate(self.ghostBeliefs)
if livingGhosts[i+1]]
"*** YOUR CODE HERE ***"
bestaction = Directions.STOP
bestdistance = None
for idx, action in enumerate(legal):
nextpos = Actions.getSuccessor(pacmanPosition, action)
for idx, dist in enumerate(livingGhostPositionDistributions):
items = dist.items()
ghostpos = sorted(items, cmp=lambda a,b: cmp(b[1], a[1]))[0][0]
dist = self.distancer.getDistance(nextpos, ghostpos)
if ((bestdistance == None) or (dist < bestdistance)):
bestdistance = dist
bestaction = action
return bestaction
class GreedyBustersAgent(BustersAgent):
"An agent that charges the closest ghost."
def registerInitialState(self, gameState):
"Pre-computes the distance between every two points."
BustersAgent.registerInitialState(self, gameState)
self.distancer = Distancer(gameState.data.layout, False)
def chooseAction(self, gameState):
"""
First computes the most likely position of each ghost that has
not yet been captured, then chooses an action that brings
Pacman closer to the closest ghost (according to mazeDistance!).
To find the mazeDistance between any two positions, use:
self.distancer.getDistance(pos1, pos2)
To find the successor position of a position after an action:
successorPosition = Actions.getSuccessor(position, action)
livingGhostPositionDistributions, defined below, is a list of
util.Counter objects equal to the position belief
distributions for each of the ghosts that are still alive. It
is defined based on (these are implementation details about
which you need not be concerned):
1) gameState.getLivingGhosts(), a list of booleans, one for each
agent, indicating whether or not the agent is alive. Note
that pacman is always agent 0, so the ghosts are agents 1,
onwards (just as before).
2) self.ghostBeliefs, the list of belief distributions for each
of the ghosts (including ghosts that are not alive). The
indices into this list should be 1 less than indices into the
gameState.getLivingGhosts() list.
"""
pacmanPosition = gameState.getPacmanPosition()
legal = [a for a in gameState.getLegalPacmanActions()]
livingGhosts = gameState.getLivingGhosts()
livingGhostPositionDistributions = \
[beliefs for i, beliefs in enumerate(self.ghostBeliefs)
if livingGhosts[i+1]]
"*** YOUR CODE HERE ***"
localMax = []
for belief in livingGhostPositionDistributions:
localMax.append(belief.argMax())
goalCoordinate, goalProbability = None, 0
for index, coordinate in enumerate(localMax):
if livingGhostPositionDistributions[index][coordinate] >= goalProbability:
goalCoordinate, goalProbability = coordinate, livingGhostPositionDistributions[index][coordinate]
tempActions = []
for action in legal:
nextLocation = Actions.getSuccessor(pacmanPosition, action)
tempActions.append((self.distancer.getDistance(nextLocation, goalCoordinate), action))
return min(tempActions)[1]
class GreedyBustersAgent(BustersAgent):
"An agent that charges the closest ghost."
def registerInitialState(self, gameState):
"Pre-computes the distance between every two points."
BustersAgent.registerInitialState(self, gameState)
self.distancer = Distancer(gameState.data.layout, False)
def chooseAction(self, gameState):
"""
First computes the most likely position of each ghost that has
not yet been captured, then chooses an action that brings
Pacman closer to the closest ghost (according to mazeDistance!).
"""
pacmanPosition = gameState.getPacmanPosition()
legal = [a for a in gameState.getLegalPacmanActions()]
livingGhosts = gameState.getLivingGhosts()
livingGhostPositionDistributions = \
[beliefs for i, beliefs in enumerate(self.ghostBeliefs)
if livingGhosts[i+1]]
"*** YOUR CODE HERE ***"
mostLikely = util.Counter()
for beliefs in livingGhostPositionDistributions:
likelyPos = beliefs.argMax()
mostLikely[likelyPos] = beliefs[likelyPos]
minDist = float('inf')
closest = None
for key in mostLikely:
curDist = self.distancer.getDistance(pacmanPosition,key)
if curDist < minDist:
minDist = curDist
closest = key
ghostPos = closest
minDist = self.distancer.getDistance(closest,pacmanPosition)
bestMove = None
for action in legal:
successorPosition = Actions.getSuccessor(pacmanPosition, action)
dist = self.distancer.getDistance(ghostPos,successorPosition)
if dist < minDist:
bestMove = action
minDist = dist
return bestMove
class GreedyBustersAgent(BustersAgent):
"An agent that charges the closest ghost."
def registerInitialState(self, gameState):
"Pre-computes the distance between every two points."
BustersAgent.registerInitialState(self, gameState)
self.distancer = Distancer(gameState.data.layout, False)
def chooseAction(self, gameState):
"""
First computes the most likely position of each ghost that has
not yet been captured, then chooses an action that brings
Pacman closer to the closest ghost (according to mazeDistance!).
"""
pacmanPosition = gameState.getPacmanPosition()
legal = [a for a in gameState.getLegalPacmanActions()]
livingGhosts = gameState.getLivingGhosts()
livingGhostPositionDistributions = \
[beliefs for i, beliefs in enumerate(self.ghostBeliefs)
if livingGhosts[i+1]]
distance = float('inf')
for ghost in livingGhostPositionDistributions:
highest_prob = -1
for key in ghost:
if ghost[key] > highest_prob:
highest_prob = ghost[key]
ghost_loc = key
new_dist = self.distancer.getDistance(pacmanPosition, ghost_loc)
if new_dist < distance:
distance = new_dist
closest = ghost_loc
distance = float('inf')
successors = []
for action in legal:
successorPosition = Actions.getSuccessor(pacmanPosition, action)
new_dist = self.distancer.getDistance(successorPosition, closest)
if new_dist < distance:
best_action = action
distance = new_dist
return best_action
class GreedyBustersAgent(BustersAgent):
"An agent that charges the closest ghost."
def registerInitialState(self, gameState):
"Pre-computes the distance between every two points."
BustersAgent.registerInitialState(self, gameState)
self.distancer = Distancer(gameState.data.layout, False)
def chooseAction(self, gameState):
"""
First computes the most likely position of each ghost that has
not yet been captured, then chooses an action that brings
Pacman closer to the closest ghost (according to mazeDistance!).
To find the mazeDistance between any two positions, use:
self.distancer.getDistance(pos1, pos2)
To find the successor position of a position after an action:
successorPosition = Actions.getSuccessor(position, action)
livingGhostPositionDistributions, defined below, is a list of
util.Counter objects equal to the position belief
distributions for each of the ghosts that are still alive. It
is defined based on (these are implementation details about
which you need not be concerned):
1) gameState.getLivingGhosts(), a list of booleans, one for each
agent, indicating whether or not the agent is alive. Note
that pacman is always agent 0, so the ghosts are agents 1,
onwards (just as before).
2) self.ghostBeliefs, the list of belief distributions for each
of the ghosts (including ghosts that are not alive). The
indices into this list should be 1 less than indices into the
gameState.getLivingGhosts() list.
"""
pacmanPosition = gameState.getPacmanPosition()
legal = [a for a in gameState.getLegalPacmanActions()]
# print legal
livingGhosts = gameState.getLivingGhosts()
# noisyDistances = gameState.getNoisyGhostDistances()
livingGhostPositionDistributions = \
[beliefs for i, beliefs in enumerate(self.ghostBeliefs)
if livingGhosts[i+1]]
for inf_mod in self.inferenceModules:
inf_mod.elapseTime(gameState)
dist = util.Counter()
for belief in livingGhostPositionDistributions:
for p, val in belief.items():
dist[p] += val
max_prob = max(dist.items(), key = lambda x: x[1])[0]
states = [(a, Actions.getSuccessor(pacmanPosition, a)) for a in legal]
actions = [(a, self.distancer.getDistance(state, max_prob)) for a, state in states]
return min(actions, key = lambda x: x[1])[0]
class GreedyBustersAgent(BustersAgent):
"""An agent that charges the closest ghost."""
def registerInitialState(self, gameState):
"""Pre-computes the distance between every two points."""
BustersAgent.registerInitialState(self, gameState)
self.distancer = Distancer(gameState.data.layout, False)
def chooseAction(self, gameState):
"""
First computes the most likely position of each ghost that has
not yet been captured, then chooses an action that brings
Pacman closer to the closest ghost (according to mazeDistance!).
To find the mazeDistance between any two positions, use:
self.distancer.getDistance(pos1, pos2)
To find the successor position of a position after an action:
successorPosition = Actions.getSuccessor(position, action)
livingGhostPositionDistributions, defined below, is a list of
util.Counter objects equal to the position belief
distributions for each of the ghosts that are still alive. It
is defined based on (these are implementation details about
which you need not be concerned):
1) gameState.getLivingGhosts(), a list of booleans, one for each
agent, indicating whether or not the agent is alive. Note
that pacman is always agent 0, so the ghosts are agents 1,
onwards (just as before).
2) self.ghostBeliefs, the list of belief distributions for each
of the ghosts (including ghosts that are not alive). The
indices into this list should be 1 less than indices into the
gameState.getLivingGhosts() list.
"""
pacmanPosition = gameState.getPacmanPosition()
actions = [a for a in gameState.getLegalPacmanActions()]
livingGhosts = gameState.getLivingGhosts()
livingGhostPositionDistributions = [beliefs for i, beliefs in enumerate(self.ghostBeliefs) if livingGhosts[i+1]]
"*** YOUR CODE HERE ***"
if len(actions) == 1:
best_action = actions[0]
else:
n_ghosts = len(livingGhostPositionDistributions)
positions = livingGhostPositionDistributions[0].iterkeys()
positionDistributions = {p: max(livingGhostPositionDistributions[i][p] for i in xrange(n_ghosts)) for p in positions}
target = max(positionDistributions.iterkeys(), key=positionDistributions.get)
def d(action):
next_position = Actions.getSuccessor(pacmanPosition, action)
return self.distancer.getDistance(next_position, target)
best_action = min(actions, key=d)
return best_action
class GreedyBustersAgent(BustersAgent):
"An agent that charges the closest ghost."
def registerInitialState(self, gameState):
"Pre-computes the distance between every two points."
BustersAgent.registerInitialState(self, gameState)
self.distancer = Distancer(gameState.data.layout, False)
def chooseAction(self, gameState):
"""
First computes the most likely position of each ghost that has
not yet been captured, then chooses an action that brings
Pacman closer to the closest ghost (according to mazeDistance!).
"""
pacmanPosition = gameState.getPacmanPosition()
legal = [a for a in gameState.getLegalPacmanActions()]
livingGhosts = gameState.getLivingGhosts()
livingGhostPositionDistributions = [
beliefs for i, beliefs in enumerate(self.ghostBeliefs) if livingGhosts[i + 1]
]
"*** YOUR CODE HERE ***"
minDistance = float("inf")
for i in range(len(livingGhostPositionDistributions)):
ghostPosition = livingGhostPositionDistributions[i].argMax()
ghostDistance = self.distancer.getDistance(pacmanPosition, ghostPosition)
if minDistance > ghostDistance:
minDistance = ghostDistance
closestPosition = ghostPosition
minDistance = float("inf")
for a in legal:
successorPosition = Actions.getSuccessor(pacmanPosition, a)
ghostDistance = self.distancer.getDistance(closestPosition, successorPosition)
if minDistance > ghostDistance:
minDistance = ghostDistance
action = a
return action
class GreedyBustersAgent(BustersAgent):
"An agent that charges the closest ghost."
def registerInitialState(self, gameState):
"Pre-computes the distance between every two points."
BustersAgent.registerInitialState(self, gameState)
self.distancer = Distancer(gameState.data.layout, False)
def chooseAction(self, gameState):
"""
First computes the most likely position of each ghost that has
not yet been captured, then chooses an action that brings
Pacman closer to the closest ghost (according to mazeDistance!).
"""
pacmanPosition = gameState.getPacmanPosition()
legal = [a for a in gameState.getLegalPacmanActions()]
livingGhosts = gameState.getLivingGhosts()
livingGhostPositionDistributions = \
[beliefs for i, beliefs in enumerate(self.ghostBeliefs)
if livingGhosts[i+1]]
#find closest position
minDistance = None
for dist in livingGhostPositionDistributions:
mostLikelyPosition = dist.argMax()
distance = self.distancer.getDistance(pacmanPosition, mostLikelyPosition)
if not minDistance or distance < minDistance:
minDistance = distance
closestPosition = mostLikelyPosition
#find best action
minDistance = None
for action in legal:
newPos = Actions.getSuccessor(pacmanPosition, action)
distanceAfterAction = self.distancer.getDistance(newPos, closestPosition)
if not minDistance or distanceAfterAction < minDistance:
minDistance = distanceAfterAction
bestAction = action
return bestAction
class GreedyBustersAgent(BustersAgent):
"An agent that charges the closest ghost."
def registerInitialState(self, gameState):
"Pre-computes the distance between every two points."
BustersAgent.registerInitialState(self, gameState)
self.distancer = Distancer(gameState.data.layout, False)
def chooseAction(self, gameState):
"""
First computes the most likely position of each ghost that has
not yet been captured, then chooses an action that brings
Pacman closest to the closest ghost (according to mazeDistance!).
"""
pacmanPosition = gameState.getPacmanPosition()
legal = [a for a in gameState.getLegalPacmanActions()]
livingGhosts = gameState.getLivingGhosts()
livingGhostPositionDistributions = \
[beliefs for i, beliefs in enumerate(self.ghostBeliefs)
if livingGhosts[i+1]]
"*** YOUR CODE HERE ***"
distance = float("inf")
ghost_position = None
for dist in livingGhostPositionDistributions:
temp_pos = dist.argMax()
temp_distance = self.distancer.getDistance(pacmanPosition, temp_pos)
if temp_distance < distance:
distance = temp_distance
ghost_position = temp_pos
dist = float("inf")
action = None
for a in legal:
succ_pos = Actions.getSuccessor(pacmanPosition, a)
temp = self.distancer.getDistance(succ_pos, ghost_position)
if temp < dist:
dist = temp
action = a
return action
class GreedyBustersAgent(BustersAgent):
"An agent that charges the closest ghost."
def registerInitialState(self, gameState):
"Pre-computes the distance between every two points."
BustersAgent.registerInitialState(self, gameState)
self.distancer = Distancer(gameState.data.layout, False)
def chooseAction(self, gameState):
pacmanPosition = gameState.getPacmanPosition()
legal = [a for a in gameState.getLegalPacmanActions()]
livingGhosts = gameState.getLivingGhosts()
livingGhostPositionDistributions = [beliefs for i,beliefs
in enumerate(self.ghostBeliefs)
if livingGhosts[i+1]]
"*** YOUR CODE HERE ***"
#Get the best distance to each ghost
best_positions = []
distances = []
for ghost_distribution in livingGhostPositionDistributions:
best_position = ghost_distribution.argMax()
distances.append(self.distancer.getDistance(pacmanPosition,best_position))
best_positions.append(best_position)
idx = distances.index(min(distances))
target = best_positions.pop(idx)
actions = []
distances = []
for action in legal:
new_position = Actions.getSuccessor(pacmanPosition,action)
distances.append(self.distancer.getDistance(new_position,target))
actions.append(action)
idx = distances.index(min(distances))
best_action = actions.pop(idx)
return best_action
class GreedyBustersAgent(BustersAgent):
"An agent that charges the closest ghost."
def registerInitialState(self, gameState):
"Pre-computes the distance between every two points."
BustersAgent.registerInitialState(self, gameState)
self.distancer = Distancer(gameState.data.layout, False)
def chooseAction(self, gameState):
"""
First computes the most likely position of each ghost that has
not yet been captured, then chooses an action that brings
Pacman closest to the closest ghost (according to mazeDistance!).
"""
pacmanPosition = gameState.getPacmanPosition()
legal = [a for a in gameState.getLegalPacmanActions()]
livingGhosts = gameState.getLivingGhosts()
livingGhostPositionDistributions = \
[beliefs for i, beliefs in enumerate(self.ghostBeliefs)
if livingGhosts[i+1]]
ghosts = [pos.argMax() for pos in livingGhostPositionDistributions]
# djikstra's algorithm that i created, myself, and not djikstra
toReturn = None
for ghost in ghosts:
distance = self.distancer.getDistance(pacmanPosition, ghost)
for action in legal:
new_dist = self.distancer.getDistance(Actions.getSuccessor(pacmanPosition, action),
ghost)
# if this is the minimum distance for the ghost's new position, choose the
# action that will get pacman closer to the ghost
if new_dist < distance:
distance = new_dist
toReturn = action
return toReturn
class GreedyBustersAgent(BustersAgent):
"An agent that charges the closest ghost."
def registerInitialState(self, gameState):
"Pre-computes the distance between every two points."
BustersAgent.registerInitialState(self, gameState)
self.distancer = Distancer(gameState.data.layout, False)
def chooseAction(self, gameState):
"""
First computes the most likely position of each ghost that
has not yet been captured, then chooses an action that brings
Pacman closer to the closest ghost (in maze distance!).
To find the maze distance between any two positions, use:
self.distancer.getDistance(pos1, pos2)
To find the successor position of a position after an action:
successorPosition = Actions.getSuccessor(position, action)
livingGhostPositionDistributions, defined below, is a list of
util.Counter objects equal to the position belief distributions
for each of the ghosts that are still alive. It is defined based
on (these are implementation details about which you need not be
concerned):
1) gameState.getLivingGhosts(), a list of booleans, one for each
agent, indicating whether or not the agent is alive. Note
that pacman is always agent 0, so the ghosts are agents 1,
onwards (just as before).
2) self.ghostBeliefs, the list of belief distributions for each
of the ghosts (including ghosts that are not alive). The
indices into this list should be 1 less than indices into the
gameState.getLivingGhosts() list.
"""
pacmanPosition = gameState.getPacmanPosition()
legal = [a for a in gameState.getLegalPacmanActions()]
livingGhosts = gameState.getLivingGhosts()
livingGhostPositionDistributions = [
beliefs for i, beliefs in enumerate(self.ghostBeliefs)
if livingGhosts[i + 1]
]
"*** YOUR CODE HERE ***"
action = legal[0]
leastDist = 1000
ghostpos = []
for counter in livingGhostPositionDistributions:
max = 0
keys = counter.keys()
pos = keys[0]
for key in keys:
if counter[key] > max:
pos = key
max = counter[key]
ghostpos.append(pos)
for next in legal:
successor = Actions.getSuccessor(pacmanPosition, next)
for myghost in ghostpos:
dist = self.distancer.getDistance(myghost, successor)
if dist < leastDist:
leastDist = dist
action = next
return action
def registerInitialState(self, gameState): "Pre-computes the distance between every two points." BustersAgent.registerInitialState(self, gameState) self.distancer = Distancer(gameState.data.layout, False)
class GreedyBustersAgent(BustersAgent):
"An agent that charges the closest ghost."
def registerInitialState(self, gameState):
"Pre-computes the distance between every two points."
BustersAgent.registerInitialState(self, gameState)
self.distancer = Distancer(gameState.data.layout, False)
def chooseAction(self, gameState):
"""
First computes the most likely position of each ghost that has
not yet been captured, then chooses an action that brings
Pacman closer to the closest ghost (according to mazeDistance!).
To find the mazeDistance between any two positions, use:
self.distancer.getDistance(pos1, pos2)
To find the successor position of a position after an action:
successorPosition = Actions.getSuccessor(position, action)
livingGhostPositionDistributions, defined below, is a list of
util.Counter objects equal to the position belief
distributions for each of the ghosts that are still alive. It
is defined based on (these are implementation details about
which you need not be concerned):
1) gameState.getLivingGhosts(), a list of booleans, one for each
agent, indicating whether or not the agent is alive. Note
that pacman is always agent 0, so the ghosts are agents 1,
onwards (just as before).
2) self.ghostBeliefs, the list of belief distributions for each
of the ghosts (including ghosts that are not alive). The
indices into this list should be 1 less than indices into the
gameState.getLivingGhosts() list.
"""
"""localMax = []
for belief in livingGhostPositionDistributions:
localMax.append(belief.argMax())
goalCoordinate, goalProbability = None, 0
for index, coordinate in enumerate(localMax):
if livingGhostPositionDistributions[index][coordinate] >= goalProbability:
goalCoordinate, goalProbability = coordinate, livingGhostPositionDistributions[index][coordinate]
temp = []
for action in legal:
nextLocation = Actions.getSuccessor(pacmanPosition, action)
temp.append((self.distancer.getDistance(nextLocation, goalCoordinate), action))
return min(temp)[1]"""
"""
pacmanPosition = gameState.getPacmanPosition()
actions = [agent for agent in gameState.getLegalPacmanActions()]
# get possible ghost position according to the distribution
live_ghosts = gameState.getLivingGhosts()
livingGhostPositionDistributions = [beliefs for i, beliefs in enumerate(self.ghostBeliefs) if live_ghosts[i+1]]
pos_ghosts = []
for distribution in livingGhostPositionDistributions:
m_dis, t_pos, t_dis = 0, 0, None
for key in distribution.keys():
if distribution[key] > m_dis:
m_dis = distribution[key]
t_pos = key
t_dis = self.distancer.getDistance(pacmanPosition, key)
pos_ghosts.append((t_pos, t_dis))
# get closest ghosts
closest_ghost = min(pos_ghosts, key=lambda x: x[1])[0]
# get the best action towards the ghost
successors = [(Actions.getSuccessor(pacmanPosition, action), action) for action in actions]
best_action = min(successors, key=lambda x: self.distancer.getDistance(x[0], closest_ghost))[1]
return best_action"""
pacman = gameState.getPacmanPosition()
legal = [a for a in gameState.getLegalPacmanActions() if a != Directions.STOP]
ghosts = gameState.getLivingGhosts()
distributions = [b for i, b in enumerate(self.ghostBeliefs) if ghosts[i + 1]]
successor = [(Actions.getSuccessor(pacman, a), a) for a in legal]
positions = [max(d.items(), key=lambda x:x[1])[0] for d in distributions]
distances = [(self.distancer.getDistance(pacman, d), d) for d in positions]
choice = min(distances)[1] # min distance between current and any ghost
actions = [(self.distancer.getDistance(choice, s[0]), s[1]) for s in successor]
action = min(actions)[1] # the action that gets us closer to the close ghost
return action
class GreedyBustersAgent(BustersAgent):
"An agent that charges the closest ghost."
def registerInitialState(self, gameState):
"Pre-computes the distance between every two points."
BustersAgent.registerInitialState(self, gameState)
self.distancer = Distancer(gameState.data.layout, False)
def chooseAction(self, gameState):
"""
First computes the most likely position of each ghost that
has not yet been captured, then chooses an action that brings
Pacman closer to the closest ghost (in maze distance!).
To find the maze distance between any two positions, use:
self.distancer.getDistance(pos1, pos2)
To find the successor position of a position after an action:
successorPosition = Actions.getSuccessor(position, action)
livingGhostPositionDistributions, defined below, is a list of
util.Counter objects equal to the position belief distributions
for each of the ghosts that are still alive. It is defined based
on (these are implementation details about which you need not be
concerned):
1) gameState.getLivingGhosts(), a list of booleans, one for each
agent, indicating whether or not the agent is alive. Note
that pacman is always agent 0, so the ghosts are agents 1,
onwards (just as before).
2) self.ghostBeliefs, the list of belief distributions for each
of the ghosts (including ghosts that are not alive). The
indices into this list should be 1 less than indices into the
gameState.getLivingGhosts() list.
"""
pacmanPosition = gameState.getPacmanPosition()
legal = [a for a in gameState.getLegalPacmanActions()]
livingGhosts = gameState.getLivingGhosts() #first one pacman self
livingGhostPositionDistributions = [
beliefs for i, beliefs in enumerate(self.ghostBeliefs)
if livingGhosts[i + 1]
]
livingGhostPos = []
closestPos = None
closestD = float('inf')
bestAction = None
for ghost in livingGhostPositionDistributions:
bestProb = 0
bestPos = None
for pos, prob in ghost.items():
if prob >= bestProb:
bestProb = prob
bestPos = pos
livingGhostPos.append(bestPos)
for x, y in livingGhostPos:
d = self.distancer.getDistance(pacmanPosition, (x, y))
if (d < closestD):
closestD = d
closestPos = (x, y)
for action in legal:
successor = Actions.getSuccessor(pacmanPosition, action)
newD = self.distancer.getDistance(successor, closestPos)
if newD < closestD:
closestD = newD
bestAction = action
return bestAction
class BustersKeyboardAgent(BustersAgent, KeyboardAgent):
"An agent controlled by the keyboard that displays beliefs about ghost positions."
def __init__(self,
index=0,
inference="KeyboardInference",
ghostAgents=None):
KeyboardAgent.__init__(self, index)
BustersAgent.__init__(self, index, inference, ghostAgents)
self.countActions = 0
def getAction(self, gameState):
return BustersAgent.getAction(self, gameState)
def chooseAction(self, gameState):
global last_move
global distWest
global distEast
global distNorth
global distSouth
distWest = 99999
distEast = 99999
distNorth = 99999
distSouth = 99999
self.distancer = Distancer(gameState.data.layout, False)
self.countActions = self.countActions + 1
last_move = KeyboardAgent.getAction(self, gameState)
# Almacenamos en variables una serie de datos utiles
legal = gameState.getLegalActions(0) ##Legal position from the pacman
posPacman = gameState.getPacmanPosition()
walls = gameState.getWalls()
livingGhosts = gameState.getLivingGhosts()
#move NORTH
if Directions.NORTH in legal:
# Inicia un contador a 1 para no tener el cuenta el pacman
iterator = 1
# Almacena la posicion del pacman resultante de ejecutar la accion
buffPacman = posPacman[0], posPacman[1] + 1
# Comprueba que la casilla objetivo no contenga un muro
if walls[buffPacman[0]][buffPacman[1]] == False:
# Itera sobre los fantasmas
for g in gameState.getGhostPositions():
# Comprueba que los fantasmas estan vivos
if livingGhosts[iterator] == True:
# Si la distancia minima actual es menor que la almacenada, se sobreescribe
if self.distancer.getDistance(g,
buffPacman) < distNorth:
distNorth = self.distancer.getDistance(
g, buffPacman)
iterator += 1
#move SOUTH
if Directions.SOUTH in legal:
# Inicia un contador a 1 para no tener el cuenta el pacman
iterator = 1
# Almacena la posicion del pacman resultante de ejecutar la accion
buffPacman = posPacman[0], posPacman[1] - 1
# Comprueba que la casilla objetivo no contenga un muro
if walls[buffPacman[0]][buffPacman[1]] == False:
# Itera sobre los fantasmas
for g in gameState.getGhostPositions():
# Comprueba que los fantasmas estan vivos
if livingGhosts[iterator] == True:
# Si la distancia minima actual es menor que la almacenada, se sobreescribe
if self.distancer.getDistance(g,
buffPacman) < distSouth:
distSouth = self.distancer.getDistance(
g, buffPacman)
iterator += 1
#move EAST
if Directions.EAST in legal:
# Inicia un contador a 1 para no tener el cuenta el pacman
iterator = 1
# Almacena la posicion del pacman resultante de ejecutar la accion
buffPacman = posPacman[0] + 1, posPacman[1]
# Comprueba que la casilla objetivo no contenga un muro
if walls[buffPacman[0]][buffPacman[1]] == False:
# Itera sobre los fantasmas
for g in gameState.getGhostPositions():
# Comprueba que los fantasmas estan vivos
if livingGhosts[iterator] == True:
# Si la distancia minima actual es menor que la almacenada, se sobreescribe
if self.distancer.getDistance(g,
buffPacman) < distEast:
distEast = self.distancer.getDistance(
g, buffPacman)
iterator += 1
#move WEST
if Directions.WEST in legal:
# Inicia un contador a 1 para no tener el cuenta el pacman
iterator = 1
# Almacena la posicion del pacman resultante de ejecutar la accion
buffPacman = posPacman[0] - 1, posPacman[1]
# Comprueba que la casilla objetivo no contenga un muro
if walls[buffPacman[0]][buffPacman[1]] == False:
# Itera sobre los fantasmas
for g in gameState.getGhostPositions():
# Comprueba que los fantasmas estan vivos
if livingGhosts[iterator] == True:
# Si la distancia minima actual es menor que la almacenada, se sobreescribe
if self.distancer.getDistance(g,
buffPacman) < distWest:
distWest = self.distancer.getDistance(
g, buffPacman)
iterator += 1
return last_move
def printLineData(self, gameState):
# En esta funcion usaremos 2 variables globales
global predictN
global prevState
# Intentamos abrir el archivo de salida, si no existe, lo creamos usando createWekaFile()
if (os.path.isfile("training_tutorial1.arff") == False):
attributesList = [["distNorth",
"NUMERIC"], ["distSouth", "NUMERIC"],
["distEast", "NUMERIC"], ["distWest", "NUMERIC"],
["Score", "NUMERIC"], ["NextScore", "NUMERIC"],
["NearestFood", "NUMERIC"],
["lastMove", "{North,South,East,West,Stop}"]]
self.createWekaFile(attributesList)
# Necesitamos la funcion distancer para hacer calculos de distancias
self.distancer = Distancer(gameState.data.layout, False)
# Si hemos llegado al turno N, imprimimos en el archivo
if self.countActions > predictN:
# Cada vez vamos a sacar 9 elementos de la lista (8 atributos + "\n")
counter = 9
# Abrimos el archivo de salida
file = open("training_tutorial1.arff", "a")
# Obtenemos la puntuacion actual, para usarla de valor predecido
prevState[5] = gameState.getScore()
while (counter > 0):
# Usamos pop para sacar uno a uno los elementos de la lista y escribirlos en el archivo de salida
x = prevState.pop(0)
file.write("%s" % (x))
# Imprimimos comas entre atributos excepto entre los 2 ultimos (last_move y "\n")
if counter > 2:
file.write(",")
counter -= 1
# Cerramos el archivo de salida
file.close()
# Metemos en la lista las variables de distancia
prevState.append(distNorth)
prevState.append(distSouth)
prevState.append(distEast)
prevState.append(distWest)
# Metemos en la lista la puntuacion actual, y un placeholder para nextScore
prevState.append(gameState.getScore())
prevState.append(gameState.getScore() - 1)
# Metemos en la lista la distancia a la comida mas cercana, si no hay, metemos 99999 en su lugar
if (gameState.getDistanceNearestFood() == None):
prevState.append(99999)
else:
prevState.append(gameState.getDistanceNearestFood())
# Introducimos el movimiento realizado, que se ha obtenido durante chooseAction()
prevState.append(last_move)
prevState.append("\n")
def createWekaFile(self, attributesList):
# Abrimos el archivo en modo append, para que no se sobreescriba el archivo
file = open("training_tutorial1.arff", "a")
# Escribimos la cabecera del archivo
file.write("@RELATION 'training_tutorial1'\n\n")
# Escribimos todos los atributos
for l in attributesList:
file.write("@ATTRIBUTE %s %s\n" % (l[0], l[1]))
# Escribimos el indicador de que empiezan los datos
file.write("\n@data\n")
def chooseAction(self, gameState):
global last_move
global distWest
global distEast
global distNorth
global distSouth
distWest = 99999
distEast = 99999
distNorth = 99999
distSouth = 99999
self.distancer = Distancer(gameState.data.layout, False)
self.countActions = self.countActions + 1
last_move = KeyboardAgent.getAction(self, gameState)
# Almacenamos en variables una serie de datos utiles
legal = gameState.getLegalActions(0) ##Legal position from the pacman
posPacman = gameState.getPacmanPosition()
walls = gameState.getWalls()
livingGhosts = gameState.getLivingGhosts()
#move NORTH
if Directions.NORTH in legal:
# Inicia un contador a 1 para no tener el cuenta el pacman
iterator = 1
# Almacena la posicion del pacman resultante de ejecutar la accion
buffPacman = posPacman[0], posPacman[1] + 1
# Comprueba que la casilla objetivo no contenga un muro
if walls[buffPacman[0]][buffPacman[1]] == False:
# Itera sobre los fantasmas
for g in gameState.getGhostPositions():
# Comprueba que los fantasmas estan vivos
if livingGhosts[iterator] == True:
# Si la distancia minima actual es menor que la almacenada, se sobreescribe
if self.distancer.getDistance(g,
buffPacman) < distNorth:
distNorth = self.distancer.getDistance(
g, buffPacman)
iterator += 1
#move SOUTH
if Directions.SOUTH in legal:
# Inicia un contador a 1 para no tener el cuenta el pacman
iterator = 1
# Almacena la posicion del pacman resultante de ejecutar la accion
buffPacman = posPacman[0], posPacman[1] - 1
# Comprueba que la casilla objetivo no contenga un muro
if walls[buffPacman[0]][buffPacman[1]] == False:
# Itera sobre los fantasmas
for g in gameState.getGhostPositions():
# Comprueba que los fantasmas estan vivos
if livingGhosts[iterator] == True:
# Si la distancia minima actual es menor que la almacenada, se sobreescribe
if self.distancer.getDistance(g,
buffPacman) < distSouth:
distSouth = self.distancer.getDistance(
g, buffPacman)
iterator += 1
#move EAST
if Directions.EAST in legal:
# Inicia un contador a 1 para no tener el cuenta el pacman
iterator = 1
# Almacena la posicion del pacman resultante de ejecutar la accion
buffPacman = posPacman[0] + 1, posPacman[1]
# Comprueba que la casilla objetivo no contenga un muro
if walls[buffPacman[0]][buffPacman[1]] == False:
# Itera sobre los fantasmas
for g in gameState.getGhostPositions():
# Comprueba que los fantasmas estan vivos
if livingGhosts[iterator] == True:
# Si la distancia minima actual es menor que la almacenada, se sobreescribe
if self.distancer.getDistance(g,
buffPacman) < distEast:
distEast = self.distancer.getDistance(
g, buffPacman)
iterator += 1
#move WEST
if Directions.WEST in legal:
# Inicia un contador a 1 para no tener el cuenta el pacman
iterator = 1
# Almacena la posicion del pacman resultante de ejecutar la accion
buffPacman = posPacman[0] - 1, posPacman[1]
# Comprueba que la casilla objetivo no contenga un muro
if walls[buffPacman[0]][buffPacman[1]] == False:
# Itera sobre los fantasmas
for g in gameState.getGhostPositions():
# Comprueba que los fantasmas estan vivos
if livingGhosts[iterator] == True:
# Si la distancia minima actual es menor que la almacenada, se sobreescribe
if self.distancer.getDistance(g,
buffPacman) < distWest:
distWest = self.distancer.getDistance(
g, buffPacman)
iterator += 1
return last_move
class GreedyBustersAgent(BustersAgent):
"An agent that charges the closest ghost."
def registerInitialState(self, gameState):
"Pre-computes the distance between every two points."
BustersAgent.registerInitialState(self, gameState)
self.distancer = Distancer(gameState.data.layout, False)
def chooseAction(self, gameState):
"""
First computes the most likely position of each ghost that
has not yet been captured, then chooses an action that brings
Pacman closer to the closest ghost (in maze distance!).
To find the maze distance between any two positions, use:
self.distancer.getDistance(pos1, pos2)
To find the successor position of a position after an action:
successorPosition = Actions.getSuccessor(position, action)
livingGhostPositionDistributions, defined below, is a list of
util.Counter objects equal to the position belief distributions
for each of the ghosts that are still alive. It is defined based
on (these are implementation details about which you need not be
concerned):
1) gameState.getLivingGhosts(), a list of booleans, one for each
agent, indicating whether or not the agent is alive. Note
that pacman is always agent 0, so the ghosts are agents 1,
onwards (just as before).
2) self.ghostBeliefs, the list of belief distributions for each
of the ghosts (including ghosts that are not alive). The
indices into this list should be 1 less than indices into the
gameState.getLivingGhosts() list.
"""
pacmanPosition = gameState.getPacmanPosition()
legal = [a for a in gameState.getLegalPacmanActions()]
livingGhosts = gameState.getLivingGhosts()
livingGhostPositionDistributions = [
beliefs for i, beliefs in enumerate(self.ghostBeliefs)
if livingGhosts[i + 1]
]
"*** YOUR CODE HERE ***"
closestDist = 999999
count = 0
positions = []
prob = 0
for ghost in livingGhostPositionDistributions:
positions.append((0, 0))
for elem in ghost:
if ghost[elem] > prob:
prob = ghost[elem]
positions[count] = elem
count += 1
prob = 0
for p in positions:
dist = self.distancer.getDistance(pacmanPosition, p)
if dist < closestDist:
closestDist = dist
closestGhost = p
for action in legal:
successorPosition = Actions.getSuccessor(pacmanPosition, action)
newDist = self.distancer.getDistance(successorPosition,
closestGhost)
if newDist < closestDist:
return action
class GreedyBustersAgent(BustersAgent):
"An agent that charges the closest ghost."
def registerInitialState(self, gameState):
"Pre-computes the distance between every two points."
BustersAgent.registerInitialState(self, gameState)
self.distancer = Distancer(gameState.data.layout, False)
def chooseAction(self, gameState):
"""
First computes the most likely position of each ghost that has
not yet been captured, then chooses an action that brings
Pacman closer to the closest ghost (according to mazeDistance!).
To find the mazeDistance between any two positions, use:
self.distancer.getDistance(pos1, pos2)
To find the successor position of a position after an action:
successorPosition = Actions.getSuccessor(position, action)
livingGhostPositionDistributions, defined below, is a list of
util.Counter objects equal to the position belief
distributions for each of the ghosts that are still alive. It
is defined based on (these are implementation details about
which you need not be concerned):
1) gameState.getLivingGhosts(), a list of booleans, one for each
agent, indicating whether or not the agent is alive. Note
that pacman is always agent 0, so the ghosts are agents 1,
onwards (just as before).
2) self.ghostBeliefs, the list of belief distributions for each
of the ghosts (including ghosts that are not alive). The
indices into this list should be 1 less than indices into the
gameState.getLivingGhosts() list.
"""
pacmanPosition = gameState.getPacmanPosition()
legal = [a for a in gameState.getLegalPacmanActions()]
livingGhosts = gameState.getLivingGhosts()
livingGhostPositionDistributions = \
[beliefs for i, beliefs in enumerate(self.ghostBeliefs)
if livingGhosts[i+1]]
"*** YOUR CODE HERE ***"
#Find most likely locations for each ghost
most_likely_locs = []
for dist in livingGhostPositionDistributions:
max_prob = 0
best_loc = 0
for loc, prob in dist.iteritems():
if (prob > max_prob):
max_prob = prob
best_loc = loc
most_likely_locs.append(best_loc)
closest_loc = most_likely_locs[0]
closest_dist = 10000000000
# Find closest likely loc
for loc in most_likely_locs:
loc_dist = self.distancer.getDistance(pacmanPosition, loc)
if (loc_dist < closest_dist):
closest_dist = loc_dist
closest_loc = loc
# Move closer to closest ghost
best_act = 0
closest_dist = 1000000000
for a in legal:
successorPosition = Actions.getSuccessor(pacmanPosition, a)
loc_dist = self.distancer.getDistance(closest_loc,
successorPosition)
if (loc_dist < closest_dist):
closest_dist = loc_dist
best_act = a
return best_act
class GreedyBustersAgent(BustersAgent):
"An agent that charges the closest ghost."
def registerInitialState(self, gameState):
"Pre-computes the distance between every two points."
BustersAgent.registerInitialState(self, gameState)
self.distancer = Distancer(gameState.data.layout, False)
def chooseAction(self, gameState):
"""
First computes the most likely position of each ghost that has
not yet been captured, then chooses an action that brings
Pacman closer to the closest ghost (according to mazeDistance!).
To find the mazeDistance between any two positions, use:
self.distancer.getDistance(pos1, pos2)
To find the successor position of a position after an action:
successorPosition = Actions.getSuccessor(position, action)
livingGhostPositionDistributions, defined below, is a list of
util.Counter objects equal to the position belief
distributions for each of the ghosts that are still alive. It
is defined based on (these are implementation details about
which you need not be concerned):
1) gameState.getLivingGhosts(), a list of booleans, one for each
agent, indicating whether or not the agent is alive. Note
that pacman is always agent 0, so the ghosts are agents 1,
onwards (just as before).
2) self.ghostBeliefs, the list of belief distributions for each
of the ghosts (including ghosts that are not alive). The
indices into this list should be 1 less than indices into the
gameState.getLivingGhosts() list.
"""
pacmanPosition = gameState.getPacmanPosition()
legal = [a for a in gameState.getLegalPacmanActions()]
livingGhosts = gameState.getLivingGhosts()
livingGhostPositionDistributions = \
[beliefs for i, beliefs in enumerate(self.ghostBeliefs)
if livingGhosts[i+1]]
"*** YOUR CODE HERE ***"
#Find likely ghost positions
ghostPos = []
for dist in livingGhostPositionDistributions:
maxProb = 0
maxPos = None
for position, probability in dist.items():
if (probability > maxProb):
maxPos = position
maxProb = probability
ghostPos.append(maxPos)
#find closest ghost
minDist = 99999
minPos = None
for ghost in ghostPos:
dist = self.distancer.getDistance(pacmanPosition, ghost)
if (dist < minDist):
minPos = ghost
minDist = dist
#find the best action
minDist = 99999
bestAction = None
for action in legal:
succPos = Actions.getSuccessor(pacmanPosition, action)
dist = self.distancer.getDistance(succPos, minPos)
if (dist < minDist):
minDist = dist
bestAction = action
return bestAction
class GreedyBustersAgent(BustersAgent):
"An agent that charges the closest ghost."
def registerInitialState(self, gameState):
"Pre-computes the distance between every two points."
BustersAgent.registerInitialState(self, gameState)
self.distancer = Distancer(gameState.data.layout, False)
def chooseAction(self, gameState):
"""
First computes the most likely position of each ghost that has
not yet been captured, then chooses an action that brings
Pacman closer to the closest ghost (according to mazeDistance!).
To find the mazeDistance between any two positions, use:
self.distancer.getDistance(pos1, pos2)
To find the successor position of a position after an action:
successorPosition = Actions.getSuccessor(position, action)
livingGhostPositionDistributions, defined below, is a list of
util.Counter objects equal to the position belief
distributions for each of the ghosts that are still alive. It
is defined based on (these are implementation details about
which you need not be concerned):
1) gameState.getLivingGhosts(), a list of booleans, one for each
agent, indicating whether or not the agent is alive. Note
that pacman is always agent 0, so the ghosts are agents 1,
onwards (just as before).
2) self.ghostBeliefs, the list of belief distributions for each
of the ghosts (including ghosts that are not alive). The
indices into this list should be 1 less than indices into the
gameState.getLivingGhosts() list.
"""
pacmanPosition = gameState.getPacmanPosition()
legal = [a for a in gameState.getLegalPacmanActions()]
livingGhosts = gameState.getLivingGhosts()
livingGhostPositionDistributions = \
[beliefs for i, beliefs in enumerate(self.ghostBeliefs)
if livingGhosts[i+1]]
"*** YOUR CODE HERE ***"
closestGhostDistance = float("inf")
ghostClosePos = None
counterForMinDistanceFromPacToGhost = dict()
for eachDistribution in livingGhostPositionDistributions:
ghostPosWithMaxProb = eachDistribution.argMax()
counterForMinDistanceFromPacToGhost[
ghostPosWithMaxProb] = self.distancer.getDistance(
pacmanPosition, ghostPosWithMaxProb)
ghostClosePos = min(counterForMinDistanceFromPacToGhost,
key=counterForMinDistanceFromPacToGhost.get)
closestGhostDistance = counterForMinDistanceFromPacToGhost[
ghostClosePos]
move = None
for action in legal:
distanceTosuccessor = self.distancer.getDistance(
Actions.getSuccessor(pacmanPosition, action), ghostClosePos)
if distanceTosuccessor < closestGhostDistance:
closestGhostDistance = distanceTosuccessor
move = action
return move
class BasicAgentAA(BustersAgent):
def registerInitialState(self, gameState):
BustersAgent.registerInitialState(self, gameState)
self.distancer = Distancer(gameState.data.layout, False)
self.countActions = 0
''' Example of counting something'''
def countFood(self, gameState):
food = 0
for width in gameState.data.food:
for height in width:
if (height == True):
food = food + 1
return food
''' Print the layout'''
def printGrid(self, gameState):
table = ""
#print(gameState.data.layout) ## Print by terminal
for x in range(gameState.data.layout.width):
for y in range(gameState.data.layout.height):
food, walls = gameState.data.food, gameState.data.layout.walls
table = table + gameState.data._foodWallStr(
food[x][y], walls[x][y]) + ","
table = table[:-1]
return table
def printInfo(self, gameState):
print "---------------- TICK ", self.countActions, " --------------------------"
# Dimensiones del mapa
width, height = gameState.data.layout.width, gameState.data.layout.height
print "Width: ", width, " Height: ", height
# Posicion del Pacman
print "Pacman position: ", gameState.getPacmanPosition()
# Acciones legales de pacman en la posicion actual
print "Legal actions: ", gameState.getLegalPacmanActions()
# Direccion de pacman
print "Pacman direction: ", gameState.data.agentStates[0].getDirection(
)
# Numero de fantasmas
print "Number of ghosts: ", gameState.getNumAgents() - 1
# Fantasmas que estan vivos (el indice 0 del array que se devuelve corresponde a pacman y siempre es false)
print "Living ghosts: ", gameState.getLivingGhosts()
# Posicion de los fantasmas
print "Ghosts positions: ", gameState.getGhostPositions()
# Direciones de los fantasmas
print "Ghosts directions: ", [
gameState.getGhostDirections().get(i)
for i in range(0,
gameState.getNumAgents() - 1)
]
# Distancia de manhattan a los fantasmas
print "Ghosts distances: ", gameState.data.ghostDistances
# Puntos de comida restantes
print "Pac dots: ", gameState.getNumFood()
# Distancia de manhattan a la comida mas cercada
print "Distance nearest pac dots: ", gameState.getDistanceNearestFood()
# Paredes del mapa
print "Map: \n", gameState.getWalls()
# Puntuacion
print "Score: ", gameState.getScore()
def chooseAction(self, gameState):
# En esta funcion usaremos 5 variables globales, una para la accion, y 4 para las distancias
global last_move
global distWest
global distEast
global distNorth
global distSouth
'''
# Incrementamos el contador de turnos
self.countActions = self.countActions + 1
# Se imprimen datos relevantes sobre la partida
self.printInfo(gameState)
# Por defecto, el movimiento a ejecutar es "Stop"
move = Directions.STOP
# Se inicializan las distancias a un numero muy alto
distWest = 99999
distEast = 99999
distNorth = 99999
distSouth = 99999
# Almacenamos en variables una serie de datos relevantes
legal = gameState.getLegalActions(0) ##Legal position from the pacman
posPacman = gameState.getPacmanPosition()
minDist = 99999
walls = gameState.getWalls()
livingGhosts = gameState.getLivingGhosts()
#move NORTH
if Directions.NORTH in legal:
# Inicia un contador a 1 para no tener el cuenta el pacman
iterator = 1
# Almacena la posicion del pacman resultante de ejecutar la accion
buffPacman = posPacman[0], posPacman[1] + 1
# Comprueba que la casilla objetivo no contenga un muro
if walls[buffPacman[0]][buffPacman[1]] == False:
# Itera sobre los fantasmas
for x in gameState.getGhostPositions():
# Comprueba que los fantasmas estan vivos
if livingGhosts[iterator] == True:
# Si la distancia actual es menor o igual que la previa de este movimiento, se sobreescribe
if self.distancer.getDistance(x, buffPacman) <= distNorth:
distNorth = self.distancer.getDistance(x, buffPacman)
# Se comprueba si la distancia es menor que la minima de todas las acciones
if distNorth < minDist:
# Se sobreescribe y se cambia el movimiento a realizar
minDist = distNorth
move = Directions.NORTH
# Si la distancia es igual a la minima actual, se comprueba si la casilla nueva contiene comida
# de ser asi, se cambia el movimiento a realizar
# Esto sirve para que en caso de haber 2 acciones igual de buenas, elija la que tiene comida
elif distNorth == minDist:
if gameState.hasFood(buffPacman[0],buffPacman[1]):
move = Directions.NORTH
iterator = iterator + 1
#move SOUTH
if Directions.SOUTH in legal:
# Inicia un contador a 1 para no tener el cuenta el pacman
iterator = 1
# Almacena la posicion del pacman resultante de ejecutar la accion
buffPacman = posPacman[0], posPacman[1] - 1
# Comprueba que la casilla objetivo no contenga un muro
if walls[buffPacman[0]][buffPacman[1]] == False:
# Itera sobre los fantasmas
for x in gameState.getGhostPositions():
# Comprueba que los fantasmas estan vivos
if livingGhosts[iterator] == True:
# Si la distancia actual es menor o igual que la previa de este movimiento, se sobreescribe
if self.distancer.getDistance(x, buffPacman) <= distSouth:
distSouth = self.distancer.getDistance(x, buffPacman)
# Se comprueba si la distancia es menor que la minima de todas las acciones
if distSouth < minDist:
# Se sobreescribe y se cambia el movimiento a realizar
minDist = distSouth
move = Directions.SOUTH
# Si la distancia es igual a la minima actual, se comprueba si la casilla nueva contiene comida
# de ser asi, se cambia el movimiento a realizar
# Esto sirve para que en caso de haber 2 acciones igual de buenas, elija la que tiene comida
elif distSouth == minDist:
if gameState.hasFood(buffPacman[0],buffPacman[1]):
move = Directions.SOUTH
iterator = iterator + 1
#move EAST
if Directions.EAST in legal:
# Inicia un contador a 1 para no tener el cuenta el pacman
iterator = 1
# Almacena la posicion del pacman resultante de ejecutar la accion
buffPacman = posPacman[0] + 1, posPacman[1]
# Comprueba que la casilla objetivo no contenga un muro
if walls[buffPacman[0]][buffPacman[1]] == False:
# Itera sobre los fantasmas
for x in gameState.getGhostPositions():
# Comprueba que los fantasmas estan vivos
if livingGhosts[iterator] == True:
# Si la distancia actual es menor o igual que la previa de este movimiento, se sobreescribe
if self.distancer.getDistance(x, buffPacman) <= distEast:
distEast = self.distancer.getDistance(x, buffPacman)
# Se comprueba si la distancia es menor que la minima de todas las acciones
if distEast < minDist:
# Se sobreescribe y se cambia el movimiento a realizar
minDist = distEast
move = Directions.EAST
# Si la distancia es igual a la minima actual, se comprueba si la casilla nueva contiene comida
# de ser asi, se cambia el movimiento a realizar
# Esto sirve para que en caso de haber 2 acciones igual de buenas, elija la que tiene comida
elif distEast == minDist:
if gameState.hasFood(buffPacman[0],buffPacman[1]):
move = Directions.EAST
iterator = iterator + 1
#move WEST
if Directions.WEST in legal:
# Inicia un contador a 1 para no tener el cuenta el pacman
iterator = 1
# Almacena la posicion del pacman resultante de ejecutar la accion
buffPacman = posPacman[0] - 1, posPacman[1]
# Comprueba que la casilla objetivo no contenga un muro
if walls[buffPacman[0]][buffPacman[1]] == False:
# Itera sobre los fantasmas
for x in gameState.getGhostPositions():
# Comprueba que los fantasmas estan vivos
if livingGhosts[iterator] == True:
# Si la distancia actual es menor o igual que la previa de este movimiento, se sobreescribe
if self.distancer.getDistance(x, buffPacman) <= distWest:
distWest = self.distancer.getDistance(x, buffPacman)
# Se comprueba si la distancia es menor que la minima de todas las acciones
if distWest < minDist:
# Se sobreescribe y se cambia el movimiento a realizar
minDist = distWest
move = Directions.WEST
# Si la distancia es igual a la minima actual, se comprueba si la casilla nueva contiene comida
# de ser asi, se cambia el movimiento a realizar
# Esto sirve para que en caso de haber 2 acciones igual de buenas, elija la que tiene comida
elif distWest == minDist:
if gameState.hasFood(buffPacman[0],buffPacman[1]):
move = Directions.WEST
iterator = iterator + 1
# Se almacena last_move para usarla en otras funciones
last_move = move
return move
'''
# Inicializamos la lista que le pasaremos a Weka
x = []
# Inicializamos las variables de distancia a un numero muy alto, que significa que la accion es ilegal
distWest = 99999
distEast = 99999
distNorth = 99999
distSouth = 99999
# Almacenamos en variables una serie de datos utiles
legal = gameState.getLegalActions(0) ##Legal position from the pacman
posPacman = gameState.getPacmanPosition()
walls = gameState.getWalls()
livingGhosts = gameState.getLivingGhosts()
#move NORTH
if Directions.NORTH in legal:
# Inicia un contador a 1 para no tener el cuenta el pacman
iterator = 1
# Almacena la posicion del pacman resultante de ejecutar la accion
buffPacman = posPacman[0], posPacman[1] + 1
# Comprueba que la casilla objetivo no contenga un muro
if walls[buffPacman[0]][buffPacman[1]] == False:
# Itera sobre los fantasmas
for g in gameState.getGhostPositions():
# Comprueba que los fantasmas estan vivos
if livingGhosts[iterator] == True:
# Si la distancia minima actual es menor que la almacenada, se sobreescribe
if self.distancer.getDistance(g,
buffPacman) < distNorth:
distNorth = self.distancer.getDistance(
g, buffPacman)
iterator += 1
#move SOUTH
if Directions.SOUTH in legal:
# Inicia un contador a 1 para no tener el cuenta el pacman
iterator = 1
# Almacena la posicion del pacman resultante de ejecutar la accion
buffPacman = posPacman[0], posPacman[1] - 1
# Comprueba que la casilla objetivo no contenga un muro
if walls[buffPacman[0]][buffPacman[1]] == False:
# Itera sobre los fantasmas
for g in gameState.getGhostPositions():
# Comprueba que los fantasmas estan vivos
if livingGhosts[iterator] == True:
# Si la distancia minima actual es menor que la almacenada, se sobreescribe
if self.distancer.getDistance(g,
buffPacman) < distSouth:
distSouth = self.distancer.getDistance(
g, buffPacman)
iterator += 1
#move EAST
if Directions.EAST in legal:
# Inicia un contador a 1 para no tener el cuenta el pacman
iterator = 1
# Almacena la posicion del pacman resultante de ejecutar la accion
buffPacman = posPacman[0] + 1, posPacman[1]
# Comprueba que la casilla objetivo no contenga un muro
if walls[buffPacman[0]][buffPacman[1]] == False:
# Itera sobre los fantasmas
for g in gameState.getGhostPositions():
# Comprueba que los fantasmas estan vivos
if livingGhosts[iterator] == True:
# Si la distancia minima actual es menor que la almacenada, se sobreescribe
if self.distancer.getDistance(g,
buffPacman) < distEast:
distEast = self.distancer.getDistance(
g, buffPacman)
iterator += 1
#move WEST
if Directions.WEST in legal:
# Inicia un contador a 1 para no tener el cuenta el pacman
iterator = 1
# Almacena la posicion del pacman resultante de ejecutar la accion
buffPacman = posPacman[0] - 1, posPacman[1]
# Comprueba que la casilla objetivo no contenga un muro
if walls[buffPacman[0]][buffPacman[1]] == False:
# Itera sobre los fantasmas
for g in gameState.getGhostPositions():
# Comprueba que los fantasmas estan vivos
if livingGhosts[iterator] == True:
# Si la distancia minima actual es menor que la almacenada, se sobreescribe
if self.distancer.getDistance(g,
buffPacman) < distWest:
distWest = self.distancer.getDistance(
g, buffPacman)
iterator += 1
# Metemos en la lista las variables de distancia
x.append(distNorth)
x.append(distSouth)
x.append(distEast)
x.append(distWest)
# -------------------------- COMMENT THIS PART IF USING NoFood_NoScore --------------------------
# Metemos en la lista la puntuacion
x.append(gameState.getScore())
# Metemos en la lista la distancia a la comida mas cercana, si no hay, metemos 99999 en su lugar
if (gameState.getDistanceNearestFood() == None):
x.append(99999)
else:
x.append(gameState.getDistanceNearestFood())
# -----------------------------------------------------------------------------------------------
# Pasamos los datos necesarios a Weka para que los clasifique
a = self.weka.predict(
"./Models/Classification/Tutorial1/Samemaps/Unfiltered/LMT.model",
x, "./Training/training_tutorial1_noNextScore.arff")
# ------ Estas lineas sirven para evitar que el agente automatico entre en un bucle infinito ----------
# El bug se produce cuando no se puede hacer la accion "East" y la accion "South" es mejor que "North"
""" if (distEast == 99999 and distNorth > distSouth and a == 'North'):
a = 'South' """
# -----------------------------------------------------------------------------------------------------
# ------ Estas lineas sirven para que el agente automatico de teclado no haga acciones ilegales -------
""" if (a == 'East' and distEast == 99999):
a = 'Stop'
elif (a == 'West' and distWest == 99999):
a = 'Stop'
elif (a == 'North' and distNorth == 99999):
a = 'Stop'
elif (a == 'South' and distSouth == 99999):
a = 'Stop' """
# ------------------------------------------------------------------------------------------------------
# Se almacena last_move para usarla en otras funciones
last_move = a
return a
def printLineData(self, gameState):
# En esta funcion usaremos 2 variables globales
global predictN
global prevState
# Intentamos abrir el archivo de salida, si no existe, lo creamos usando createWekaFile()
if (os.path.isfile("training_tutorial1.arff") == False):
attributesList = [["distNorth",
"NUMERIC"], ["distSouth", "NUMERIC"],
["distEast", "NUMERIC"], ["distWest", "NUMERIC"],
["Score", "NUMERIC"], ["NextScore", "NUMERIC"],
["NearestFood", "NUMERIC"],
["lastMove", "{North,South,East,West,Stop}"]]
self.createWekaFile(attributesList)
# Si hemos llegado al turno N, imprimimos en el archivo
if self.countActions > predictN:
# Cada vez vamos a sacar 9 elementos de la lista (8 atributos + "\n")
counter = 9
# Abrimos el archivo de salida
file = open("training_tutorial1.arff", "a")
# Obtenemos la puntuacion actual, para usarla de valor predecido
prevState[5] = gameState.getScore()
while (counter > 0):
# Usamos pop para sacar uno a uno los elementos de la lista y escribirlos en el archivo de salida
x = prevState.pop(0)
file.write("%s" % (x))
# Imprimimos comas entre atributos excepto entre los 2 ultimos (last_move y "\n")
if counter > 2:
file.write(",")
counter -= 1
# Cerramos el archivo de salida
file.close()
# Metemos en la lista las variables de distancia
prevState.append(distNorth)
prevState.append(distSouth)
prevState.append(distEast)
prevState.append(distWest)
# Metemos en la lista la puntuacion actual, y un placeholder para nextScore
prevState.append(gameState.getScore())
prevState.append(gameState.getScore() - 1)
# Metemos en la lista la distancia a la comida mas cercana, si no hay, metemos 99999 en su lugar
if (gameState.getDistanceNearestFood() == None):
prevState.append(99999)
else:
prevState.append(gameState.getDistanceNearestFood())
# Introducimos el movimiento realizado, que se ha obtenido durante chooseAction()
prevState.append(last_move)
prevState.append("\n")
def createWekaFile(self, attributesList):
# Abrimos el archivo en modo append, para que no se sobreescriba el archivo
file = open("training_tutorial1.arff", "a")
# Escribimos la cabecera del archivo
file.write("@RELATION 'training_tutorial1'\n\n")
# Escribimos todos los atributos
for l in attributesList:
file.write("@ATTRIBUTE %s %s\n" % (l[0], l[1]))
# Escribimos el indicador de que empiezan los datos
file.write("\n@data\n")
class GreedyBustersAgent(BustersAgent):
"An agent that charges the closest ghost."
def registerInitialState(self, gameState):
"Pre-computes the distance between every two points."
BustersAgent.registerInitialState(self, gameState)
self.distancer = Distancer(gameState.data.layout, False)
def chooseAction(self, gameState):
"""
First computes the most likely position of each ghost that has
not yet been captured, then chooses an action that brings
Pacman closer to the closest ghost (according to mazeDistance!).
To find the mazeDistance between any two positions, use:
self.distancer.getDistance(pos1, pos2)
To find the successor position of a position after an action:
successorPosition = Actions.getSuccessor(position, action)
livingGhostPositionDistributions, defined below, is a list of
util.Counter objects equal to the position belief
distributions for each of the ghosts that are still alive. It
is defined based on (these are implementation details about
which you need not be concerned):
1) gameState.getLivingGhosts(), a list of booleans, one for each
agent, indicating whether or not the agent is alive. Note
that pacman is always agent 0, so the ghosts are agents 1,
onwards (just as before).
2) self.ghostBeliefs, the list of belief distributions for each
of the ghosts (including ghosts that are not alive). The
indices into this list should be 1 less than indices into the
gameState.getLivingGhosts() list.
"""
pacmanPosition = gameState.getPacmanPosition()
legal = [a for a in gameState.getLegalPacmanActions()]
livingGhosts = gameState.getLivingGhosts()
livingGhostPositionDistributions = \
[beliefs for i, beliefs in enumerate(self.ghostBeliefs)
if livingGhosts[i+1]]
probably = [] # posição mais provável de cada fantasma vivo
for pos in livingGhostPositionDistributions:
probably.append(pos.argMax())
action = util.Counter() # guardaremos a menor distancia após tal acao
#inicializando
for pos in legal:
action[pos] = 98765432109876543210 # infinito
for acao in action.keys():
successorPosition = Actions.getSuccessor(pacmanPosition, acao) # atualiza posicao depois da acao
for p in probably:
mazeDist = self.distancer.getDistance(successorPosition, p)
if mazeDist < action[acao]: # verifica se é menor que a menor distância
action[acao] = mazeDist
# invertendo sinais do argMax()
for acao in action.keys():
action[acao] = action[acao] * - 1
return action.argMax()
class GreedyBustersAgent(BustersAgent):
"An agent that charges the closest ghost."
def registerInitialState(self, gameState):
"Pre-computes the distance between every two points."
BustersAgent.registerInitialState(self, gameState)
self.distancer = Distancer(gameState.data.layout, False)
def chooseAction(self, gameState):
"""
First computes the most likely position of each ghost that has
not yet been captured, then chooses an action that brings
Pacman closer to the closest ghost (according to mazeDistance!).
To find the mazeDistance between any two positions, use:
self.distancer.getDistance(pos1, pos2)
To find the successor position of a position after an action:
successorPosition = Actions.getSuccessor(position, action)
livingGhostPositionDistributions, defined below, is a list of
util.Counter objects equal to the position belief
distributions for each of the ghosts that are still alive. It
is defined based on (these are implementation details about
which you need not be concerned):
1) gameState.getLivingGhosts(), a list of booleans, one for each
agent, indicating whether or not the agent is alive. Note
that pacman is always agent 0, so the ghosts are agents 1,
onwards (just as before).
2) self.ghostBeliefs, the list of belief distributions for each
of the ghosts (including ghosts that are not alive). The
indices into this list should be 1 less than indices into the
gameState.getLivingGhosts() list.
"""
pacmanPosition = gameState.getPacmanPosition()
pacmanLegalAction = [a for a in gameState.getLegalPacmanActions()]
livingGhosts = gameState.getLivingGhosts()
livingGhostPositionDistributions = [
beliefs for i, beliefs in enumerate(self.ghostBeliefs)
if livingGhosts[i + 1]
]
"*** YOUR CODE HERE ***"
# Chase the ghost nearest to pacman
# return action = argmin(action.distance)
minDistance = float("inf")
bestAction = None
for action in pacmanLegalAction:
successorPosition = Actions.getSuccessor(pacmanPosition, action)
for ghostPositionDistribution in livingGhostPositionDistributions:
# Guess the ghostposition = argmax(Prob[position])
ghostPosition = ghostPositionDistribution.sortedKeys()[0]
distance = self.distancer.getDistance(successorPosition,
ghostPosition)
if distance < minDistance:
minDistance = distance
bestAction = action
return bestAction
class P3QLearning(BustersAgent):
"An agent that charges the closest ghost."
def __init__(self, index=0, inference="ExactInference", ghostAgents=None):
BustersAgent.__init__(self, index, inference, ghostAgents)
self.q_table = self.initQTable()
self.epsilon = 0.3
self.alpha = 0.8
self.discount = 0.8
self.actions = [
Directions.NORTH, Directions.WEST, Directions.SOUTH,
Directions.EAST
]
self.lastState = None
self.lastAction = None
self.numGhosts = 4
self.lastDistance = 100
self.turns = 0
self.reward = 0
#para cada par q_table[(state, action)] habra un valor.
def registerInitialState(self, gameState):
BustersAgent.registerInitialState(self, gameState)
self.distancer = Distancer(gameState.data.layout, False)
def getAction(self, gameState):
return self.chooseAction(gameState)
def getState(self, gameState):
self.reward = 0
state = ""
ghostDist = []
for i in range(len(gameState.livingGhosts)):
if gameState.livingGhosts[i] is True:
ghostDist.append(gameState.getGhostPosition(i))
pacmanPosition = gameState.getPacmanPosition()
dists = []
for i in ghostDist:
dists.append(self.distancer.getDistance(pacmanPosition, i))
#get the index of the nearest ghost
index = dists.index(min(dists))
if min(dists) < self.lastDistance:
self.reward = 5
#get the vector between pacman and the nearest ghost
vec = (pacmanPosition[0] - ghostDist[index][0],
pacmanPosition[1] - ghostDist[index][1])
if vec[0] > 0:
if vec[1] > 0:
#print "down left",
if abs(vec[0]) > abs(vec[1]):
state += Directions.WEST
else:
state += Directions.SOUTH
else:
#print "up left",
if abs(vec[0]) > abs(vec[1]):
state += Directions.WEST
else:
state += Directions.NORTH
else:
if vec[1] > 0:
#print "down right",
if abs(vec[0]) > abs(vec[1]):
state += Directions.EAST
else:
state += Directions.SOUTH
else:
#print "up right",
if abs(vec[0]) > abs(vec[1]):
state += Directions.EAST
else:
state += Directions.NORTH
state += ","
state +=\
str(gameState.hasWall(gameState.getPacmanPosition()[0], gameState.getPacmanPosition()[1] + 1)) + "," +\
str(gameState.hasWall(gameState.getPacmanPosition()[0] - 1, gameState.getPacmanPosition()[1])) + "," +\
str(gameState.hasWall(gameState.getPacmanPosition()[0], gameState.getPacmanPosition()[1] - 1)) + "," +\
str(gameState.hasWall(gameState.getPacmanPosition()[0] + 1, gameState.getPacmanPosition()[1]))
return state
def shouldExit(self):
return self.turns >= 800
def chooseAction(self, gameState):
#if the number of turns is bigger than a constant
#if self.shouldExit():
# sys.exit(0)
#get the current state
state = self.getState(gameState)
#get the action
legalActions = self.getLegalActions(state)
action = None
if util.flipCoin(self.epsilon):
action = self.getPolicy(state)
else:
action = random.choice(legalActions)
#update the table
if self.lastState != None and self.lastAction != None:
#if a ghost has been eaten
if sum(gameState.livingGhosts) < self.numGhosts:
numGhosts = sum(gameState.livingGhosts)
self.reward = 100
self.update(self.lastState, self.lastAction, state, self.reward)
#update values
self.lastState = state
self.lastAction = action
self.turns += 1
return action
def getPolicy(self, state):
return self.computeActionFromQValues(state)
def getLegalActions(self, state):
legalActions = []
state = state.split(",")[1:]
for i, s in enumerate(state):
if s == "False":
legalActions.append(self.actions[i])
return legalActions
def getValue(self, state):
return self.computeValueFromQValues(state)
def computeValueFromQValues(self, state):
"""
Returns max_action Q(state,action)
where the max is over legal actions. Note that if
there are no legal actions, which is the case at the
terminal state, you should return a value of 0.0.
"""
legalActions = self.getLegalActions(state)
if len(legalActions) == 0:
return 0.0
tmp = []
for action in legalActions:
tmp.append(self.computeQValueFromValues(state, action))
return max(tmp)
def computeActionFromQValues(self, state):
"""
Compute the best action to take in a state. Note that if there
are no legal actions, which is the case at the terminal state,
you should return None.
"""
legalActions = self.getLegalActions(state)
if len(legalActions) == 0:
return None
tmp = util.Counter()
for action in legalActions:
tmp[action] = self.computeQValueFromValues(state, action)
return tmp.argMax()
def computeQValueFromValues(self, state, action):
"""
Returns Q(state,action)
Should return 0.0 if we have never seen a state
or the Q node value otherwise
"""
"*** YOUR CODE HERE ***"
return self.q_table[(state, action)]
def initQTable(self):
table_file = open("qtable.txt", "r")
table_file.seek(0)
table = table_file.readlines()
qvalues = []
for i, line in enumerate(table):
qvalues.append(line)
table_file.close()
q_table = util.Counter()
dirs = [
Directions.NORTH, Directions.SOUTH, Directions.EAST,
Directions.WEST
]
walls = ["True", "False"]
actions = [
Directions.NORTH, Directions.SOUTH, Directions.EAST,
Directions.WEST
]
i = 0
for direction in dirs:
for wall1 in walls:
for wall2 in walls:
for wall3 in walls:
for wall4 in walls:
for action in actions:
state = direction + "," + wall1 + "," + wall2 + "," + wall3 + "," + wall4
q_table[(state, action)] = float(qvalues[i])
i += 1
return q_table
def update(self, state, action, nextState, reward):
self.q_table[(state,action)] = (1 - self.alpha) * self.q_table[(state, action)] +\
self.alpha * (reward + self.discount*self.getValue(nextState))
def writeQtable(self):
table_file = open("qtable.txt", "w+")
for key in self.q_table:
table_file.write(str(self.q_table[key]) + "\n")
table_file.close()
def __del__(self):
self.writeQtable()
class GreedyBustersAgent(BustersAgent):
"An agent that charges the closest ghost."
def registerInitialState(self, gameState):
"Pre-computes the distance between every two points."
BustersAgent.registerInitialState(self, gameState)
self.distancer = Distancer(gameState.data.layout, False)
def chooseAction(self, gameState):
"""
First computes the most likely position of each ghost that has
not yet been captured, then chooses an action that brings
Pacman closer to the closest ghost (according to mazeDistance!).
To find the mazeDistance between any two positions, use:
self.distancer.getDistance(pos1, pos2)
To find the successor position of a position after an action:
successorPosition = Actions.getSuccessor(position, action)
livingGhostPositionDistributions, defined below, is a list of
util.Counter objects equal to the position belief
distributions for each of the ghosts that are still alive. It
is defined based on (these are implementation details about
which you need not be concerned):
1) gameState.getLivingGhosts(), a list of booleans, one for each
agent, indicating whether or not the agent is alive. Note
that pacman is always agent 0, so the ghosts are agents 1,
onwards (just as before).
2) self.ghostBeliefs, the list of belief distributions for each
of the ghosts (including ghosts that are not alive). The
indices into this list should be 1 less than indices into the
gameState.getLivingGhosts() list.
"""
pacmanPosition = gameState.getPacmanPosition()
legal = [a for a in gameState.getLegalPacmanActions()]
livingGhosts = gameState.getLivingGhosts()
livingGhostPositionDistributions = \
[beliefs for i, beliefs in enumerate(self.ghostBeliefs)
if livingGhosts[i+1]]
"*** YOUR CODE HERE ***"
#util.raiseNotDefined()
mindistance = 10e4
nextactiontochose = Directions.STOP
Total_number_of_ghosts = 0
for i in range(1, len(livingGhosts)):
if (livingGhosts[i]) == True:
Total_number_of_ghosts = Total_number_of_ghosts + 1
finalghostpos = 0
probofghost = 0
for action in legal:
npos = Actions.getSuccessor(pacmanPosition, action)
for j in range(Total_number_of_ghosts):
currentdistributionofghost = livingGhostPositionDistributions[
j]
for ghostposition in currentdistributionofghost.keys():
if currentdistributionofghost[ghostposition] > probofghost:
finalghostpos = ghostposition
probofghost = currentdistributionofghost[ghostposition]
ghostdistance = self.distancer.getDistance(npos, finalghostpos)
if ghostdistance < mindistance:
mindistance = ghostdistance
nextactiontochose = action
return nextactiontochose
class GreedyBustersAgent(BustersAgent):
"An agent that charges the closest ghost."
def registerInitialState(self, gameState):
"Pre-computes the distance between every two points."
BustersAgent.registerInitialState(self, gameState)
self.distancer = Distancer(gameState.data.layout, False)
def chooseAction(self, gameState):
"""
First computes the most likely position of each ghost that
has not yet been captured, then chooses an action that brings
Pacman closer to the closest ghost (in maze distance!).
To find the maze distance between any two positions, use:
self.distancer.getDistance(pos1, pos2)
To find the successor position of a position after an action:
successorPosition = Actions.getSuccessor(position, action)
livingGhostPositionDistributions, defined below, is a list of
util.Counter objects equal to the position belief distributions
for each of the ghosts that are still alive. It is defined based
on (these are implementation details about which you need not be
concerned):
1) gameState.getLivingGhosts(), a list of booleans, one for each
agent, indicating whether or not the agent is alive. Note
that pacman is always agent 0, so the ghosts are agents 1,
onwards (just as before).
2) self.ghostBeliefs, the list of belief distributions for each
of the ghosts (including ghosts that are not alive). The
indices into this list should be 1 less than indices into the
gameState.getLivingGhosts() list.
You may remove Directions.STOP from the list of available actions.
"""
pacmanPosition = gameState.getPacmanPosition()
legal = [
a for a in gameState.getLegalPacmanActions()
if a != Directions.STOP
]
livingGhosts = gameState.getLivingGhosts()
livingGhostPositionDistributions = [
beliefs for i, beliefs in enumerate(self.ghostBeliefs)
if livingGhosts[i + 1]
]
"*** YOUR CODE HERE ***"
closestGhostDistance = float("inf")
closestGhostPosition = None
for distribution in livingGhostPositionDistributions:
probability = 0
maxProbPos = None
for d in distribution:
if distribution[d] > probability:
probability = distribution[d]
maxProbPos = d
currDist = self.distancer.getDistance(pacmanPosition, maxProbPos)
if currDist < closestGhostDistance:
closestGhostDistance = currDist
closestGhostPosition = maxProbPos
"Now that we have the probabalistic closest ghost position, we need to pick a move"
"that minimizes the gap"
minDist = closestGhostDistance
bestMove = None
"this loop takes a random best move (in case multiple happen to close the distance equally"
for action in legal:
succPos = Actions.getSuccessor(pacmanPosition, action)
succDist = self.distancer.getDistance(succPos,
closestGhostPosition)
if succDist < minDist:
minDist = succDist
bestMove = action
elif succDist == minDist:
bestMove = random.choice([bestMove, action])
return bestMove
util.raiseNotDefined()
class GreedyBustersAgent(BustersAgent):
"An agent that charges the closest ghost."
def registerInitialState(self, gameState):
"Pre-computes the distance between every two points."
BustersAgent.registerInitialState(self, gameState)
self.distancer = Distancer(gameState.data.layout, False)
def chooseAction(self, gameState):
"""
First computes the most likely position of each ghost that
has not yet been captured, then chooses an action that brings
Pacman closer to the closest ghost (in maze distance!).
To find the maze distance between any two positions, use:
self.distancer.getDistance(pos1, pos2)
To find the successor position of a position after an action:
successorPosition = Actions.getSuccessor(position, action)
livingGhostPositionDistributions, defined below, is a list of
util.Counter objects equal to the position belief distributions
for each of the ghosts that are still alive. It is defined based
on (these are implementation details about which you need not be
concerned):
1) gameState.getLivingGhosts(), a list of booleans, one for each
agent, indicating whether or not the agent is alive. Note
that pacman is always agent 0, so the ghosts are agents 1,
onwards (just as before).
2) self.ghostBeliefs, the list of belief distributions for each
of the ghosts (including ghosts that are not alive). The
indices into this list should be 1 less than indices into the
gameState.getLivingGhosts() list.
"""
pacmanPosition = gameState.getPacmanPosition()
legal = [a for a in gameState.getLegalPacmanActions()]
livingGhosts = gameState.getLivingGhosts()
livingGhostPositionDistributions = [beliefs for i,beliefs
in enumerate(self.ghostBeliefs)
if livingGhosts[i+1]]
maxPosList = util.Counter()
for posList in livingGhostPositionDistributions:
maxProb = 0
for pos, prob in posList.items():
if prob > maxProb:
maxProb = prob
maxPos = pos
maxPosList[maxPos] = self.distancer.getDistance(maxPos, pacmanPosition)
maxPosList.sortedKeys()
minPos, dis = maxPosList.items()[len(maxPosList)-1]
currDistance = self.distancer.getDistance(pacmanPosition, minPos)
for action in legal:
nextPacPos = Actions.getSuccessor(pacmanPosition, action)
nextDistance = self.distancer.getDistance(nextPacPos, minPos)
#print "currDistance", currDistance, "nextDistance", nextDistance
if nextDistance < currDistance:
return action
for action in legal:
if action == "Stop":
continue
nextPacPos = Actions.getSuccessor(pacmanPosition, action)
nextDistance = self.distancer.getDistance(nextPacPos, minPos)
#print "currDistance", currDistance, "nextDistance", nextDistance
if nextDistance == currDistance:
return action
return legal[0]
class GreedyBustersAgent(BustersAgent):
"An agent that charges the closest ghost."
def registerInitialState(self, gameState):
"Pre-computes the distance between every two points."
BustersAgent.registerInitialState(self, gameState)
self.distancer = Distancer(gameState.data.layout, False)
def chooseAction(self, gameState):
"""
First computes the most likely position of each ghost that has
not yet been captured, then chooses an action that brings
Pacman closer to the closest ghost (according to mazeDistance!).
To find the mazeDistance between any two positions, use:
self.distancer.getDistance(pos1, pos2)
To find the successor position of a position after an action:
successorPosition = Actions.getSuccessor(position, action)
livingGhostPositionDistributions, defined below, is a list of
util.Counter objects equal to the position belief
distributions for each of the ghosts that are still alive. It
is defined based on (these are implementation details about
which you need not be concerned):
1) gameState.getLivingGhosts(), a list of booleans, one for each
agent, indicating whether or not the agent is alive. Note
that pacman is always agent 0, so the ghosts are agents 1,
onwards (just as before).
2) self.ghostBeliefs, the list of belief distributions for each
of the ghosts (including ghosts that are not alive). The
indices into this list should be 1 less than indices into the
gameState.getLivingGhosts() list.
"""
pacmanPosition = gameState.getPacmanPosition()
legal = [a for a in gameState.getLegalPacmanActions()]
livingGhosts = gameState.getLivingGhosts()
livingGhostPositionDistributions = \
[beliefs for i, beliefs in enumerate(self.ghostBeliefs)
if livingGhosts[i+1]]
"*** YOUR CODE HERE ***"
maxPosList = []
minDist = float("inf")
minDistIndex = None
for beliefs in livingGhostPositionDistributions:
maxPosList.append(beliefs.argMax())
for maxPos in range(len(maxPosList)):
minDistance = self.distancer.getDistance(maxPosList[maxPos],
pacmanPosition)
if minDistIndex is None or minDistance <= minDist:
minDistIndex = maxPos
minDist = minDistance
minPos = maxPosList[minDistIndex]
newMinDist = float("inf")
bestAction = None
for a in (legal):
newPos = Actions.getSuccessor(pacmanPosition, a)
mazeDistance = self.distancer.getDistance(minPos, newPos)
if bestAction is None or mazeDistance <= newMinDist:
bestAction = a
newMinDist = mazeDistance
return bestAction
class GreedyBustersAgent(BustersAgent):
"An agent that charges the closest ghost."
def registerInitialState(self, gameState):
"Pre-computes the distance between every two points."
BustersAgent.registerInitialState(self, gameState)
self.distancer = Distancer(gameState.data.layout, False)
def chooseAction(self, gameState):
"""
First computes the most likely position of each ghost that has
not yet been captured, then chooses an action that brings
Pacman closer to the closest ghost (according to mazeDistance!).
To find the mazeDistance between any two positions, use:
self.distancer.getDistance(pos1, pos2)
To find the successor position of a position after an action:
successorPosition = Actions.getSuccessor(position, action)
livingGhostPositionDistributions, defined below, is a list of
util.Counter objects equal to the position belief
distributions for each of the ghosts that are still alive. It
is defined based on (these are implementation details about
which you need not be concerned):
1) gameState.getLivingGhosts(), a list of booleans, one for each
agent, indicating whether or not the agent is alive. Note
that pacman is always agent 0, so the ghosts are agents 1,
onwards (just as before).
2) self.ghostBeliefs, the list of belief distributions for each
of the ghosts (including ghosts that are not alive). The
indices into this list should be 1 less than indices into the
gameState.getLivingGhosts() list.
"""
pacmanPosition = gameState.getPacmanPosition()
print(pacmanPosition)
legal = [a for a in gameState.getLegalPacmanActions()]
livingGhosts = gameState.getLivingGhosts()
print(legal, livingGhosts)
livingGhostPositionDistributions = self.ghostBeliefs
"*** YOUR CODE HERE ***"
# distance + probability*lambda
total = 0.0
maxList = []
for i in range(len(livingGhostPositionDistributions)):
print(i, livingGhostPositionDistributions[i], '\n')
argmax = 0.0
maxPo = pacmanPosition
for point in livingGhostPositionDistributions[i]:
prb = livingGhostPositionDistributions[i][point]
if prb > argmax:
argmax = prb
maxPo = point
if livingGhosts[i + 1]:
maxList.append(maxPo)
print(pacmanPosition, maxList)
maxMove = 10000
choice = legal[0]
for move in legal:
newPos = Actions.getSuccessor(pacmanPosition, move)
for dis in maxList:
dist = self.distancer.getDistance(dis, newPos)
if dist < maxMove:
maxMove = dist
choice = move
return choice
class GreedyBustersAgent(BustersAgent):
"An agent that charges the closest ghost."
def registerInitialState(self, gameState):
"Pre-computes the distance between every two points."
BustersAgent.registerInitialState(self, gameState)
self.distancer = Distancer(gameState.data.layout, False)
def chooseAction(self, gameState):
"""
First computes the most likely position of each ghost that has
not yet been captured, then chooses an action that brings
Pacman closer to the closest ghost (according to mazeDistance!).
To find the mazeDistance between any two positions, use:
self.distancer.getDistance(pos1, pos2)
To find the successor position of a position after an action:
successorPosition = Actions.getSuccessor(position, action)
livingGhostPositionDistributions, defined below, is a list of
util.Counter objects equal to the position belief
distributions for each of the ghosts that are still alive. It
is defined based on (these are implementation details about
which you need not be concerned):
1) gameState.getLivingGhosts(), a list of booleans, one for each
agent, indicating whether or not the agent is alive. Note
that pacman is always agent 0, so the ghosts are agents 1,
onwards (just as before).
2) self.ghostBeliefs, the list of belief distributions for each
of the ghosts (including ghosts that are not alive). The
indices into this list should be 1 less than indices into the
gameState.getLivingGhosts() list.
"""
pacmanPosition = gameState.getPacmanPosition()
legal = [a for a in gameState.getLegalPacmanActions()]
livingGhosts = gameState.getLivingGhosts()
livingGhostPositionDistributions = \
[beliefs for i, beliefs in enumerate(self.ghostBeliefs)
if livingGhosts[i+1]]
'''What we will end up returning as our best "Greedy" action'''
actionWithMinimalDistance = 99999
actionTaken = None
for action in legal:
'''Everywhere we can go from our current position'''
newPosition = Actions.getSuccessor(pacmanPosition, action)
ghostPosition = (-1, -1)
ghostProbability = -1
'''Loop through all the ghosts'''
for ghost in livingGhostPositionDistributions:
'''Loop through the probabilities for each ghost'''
for posProb in ghost:
if (ghost[posProb] > ghostProbability):
ghostProbability = ghost[posProb]
ghostPosition = posProb
'''
Ok, so now we have the ghost with which we think we know the position with most likelihood.
This is where we need to:
- Compute the distance from ourselves to the position we found, then update the best action if applica-
ble
'''
distFromSuccessorToGhost = self.distancer.getDistance(
newPosition, ghostPosition)
if (distFromSuccessorToGhost < actionWithMinimalDistance):
actionWithMinimalDistance = distFromSuccessorToGhost
actionTaken = action
return actionTaken
class GreedyBustersAgent(BustersAgent):
"An agent that charges the closest ghost."
def registerInitialState(self, gameState):
"Pre-computes the distance between every two points."
BustersAgent.registerInitialState(self, gameState)
self.distancer = Distancer(gameState.data.layout, False)
def chooseAction(self, gameState):
"""
First computes the most likely position of each ghost that has
not yet been captured, then chooses an action that brings
Pacman closer to the closest ghost (according to mazeDistance!).
To find the mazeDistance between any two positions, use:
self.distancer.getDistance(pos1, pos2)
To find the successor position of a position after an action:
successorPosition = Actions.getSuccessor(position, action)
livingGhostPositionDistributions, defined below, is a list of
util.Counter objects equal to the position belief
distributions for each of the ghosts that are still alive. It
is defined based on (these are implementation details about
which you need not be concerned):
1) gameState.getLivingGhosts(), a list of booleans, one for each
agent, indicating whether or not the agent is alive. Note
that pacman is always agent 0, so the ghosts are agents 1,
onwards (just as before).
2) self.ghostBeliefs, the list of belief distributions for each
of the ghosts (including ghosts that are not alive). The
indices into this list should be 1 less than indices into the
gameState.getLivingGhosts() list.
"""
pacmanPosition = gameState.getPacmanPosition()
legal = [a for a in gameState.getLegalPacmanActions()]
livingGhosts = gameState.getLivingGhosts()
livingGhostPositionDistributions = \
[beliefs for i, beliefs in enumerate(self.ghostBeliefs)
if livingGhosts[i + 1]]
maxTemp = []
index = 0
while index < len(livingGhostPositionDistributions):
maxTemp.append(livingGhostPositionDistributions[index].argMax())
index += 1
goal, probGoal = None, 0
i = 0
while i < len(maxTemp):
if livingGhostPositionDistributions[i][maxTemp[i]] >= probGoal:
goal = maxTemp[i]
probGoal = livingGhostPositionDistributions[i][maxTemp[i]]
i += 1
j = 0
temp = []
while j < len(legal):
temp.append((self.distancer.getDistance(
Actions.getSuccessor(pacmanPosition, legal[j]),
goal), legal[j]))
j += 1
return min(temp)[1]
class GreedyBustersAgent(BustersAgent):
"An agent that charges the closest ghost."
def registerInitialState(self, gameState):
"Pre-computes the distance between every two points."
BustersAgent.registerInitialState(self, gameState)
self.distancer = Distancer(gameState.data.layout, False)
def chooseAction(self, gameState):
"""
First computes the most likely position of each ghost that has
not yet been captured, then chooses an action that brings
Pacman closer to the closest ghost (according to mazeDistance!).
To find the mazeDistance between any two positions, use:
self.distancer.getDistance(pos1, pos2)
To find the successor position of a position after an action:
successorPosition = Actions.getSuccessor(position, action)
livingGhostPositionDistributions, defined below, is a list of
util.Counter objects equal to the position belief
distributions for each of the ghosts that are still alive. It
is defined based on (these are implementation details about
which you need not be concerned):
1) gameState.getLivingGhosts(), a list of booleans, one for each
agent, indicating whether or not the agent is alive. Note
that pacman is always agent 0, so the ghosts are agents 1,
onwards (just as before).
2) self.ghostBeliefs, the list of belief distributions for each
of the ghosts (including ghosts that are not alive). The
indices into this list should be 1 less than indices into the
gameState.getLivingGhosts() list.
"""
# Renamed variables
pacman_pos = gameState.getPacmanPosition()
legal_actions = [a for a in gameState.getLegalPacmanActions()]
live_ghosts = gameState.getLivingGhosts()
living_ghost_pos_dists = [
beliefs for i, beliefs in enumerate(self.ghostBeliefs)
if live_ghosts[i + 1]
]
"*** YOUR CODE HERE ***"
# Map ghosts to most likely positions
most_likely_ghost_positions = [
dist.argMax() for dist in living_ghost_pos_dists
]
# Maps moves to distances from most likely positions
moves_to_distances = {
move: [
self.distancer.getDistance(
Actions.getSuccessor(pacman_pos, move), most_likely_pos)
for most_likely_pos in most_likely_ghost_positions
]
for move in legal_actions
}
# Pick the move with the smallest minimal distance
return min(moves_to_distances.keys(),
key=lambda move: min(moves_to_distances[move]))
class ClusterAgent (BustersAgent):
def registerInitialState(self, gameState):
BustersAgent.registerInitialState(self, gameState)
self.distancer = Distancer(gameState.data.layout, False)
#Definimos si se usa la distancia (true para v1 y v2, false para v3)
self.dis = True
#Para calcular los valores de la clase en las politicas.
self.clusters = 8
self.classes = 4
self.classCounts = [[0 for i in range(self.classes)]for j in range(self.clusters)]
self.classIndex = 2
self.clusterIndex = 3
self.readInstances()
#Esto nos servira para guardar las instancias de entrenamiento.
self.numInstances = 52
self.numAttributes = 4
#self.instances = [[" " for i in range(self.numAttributes)] for j in range(self.numInstances)]
self.ins = [" " for i in range(self.numInstances)]
#Para usar la libreria debemos usar la maquina virtual de java, JVM
jvm.start()
#Creamos el modelo
loader = Loader(classname="weka.core.converters.ArffLoader")
data = loader.load_file("/home/dot/Escritorio/Universidad/Machine Learning/practica 2/Outputs/agent_header.arff")
self.clusterer = Clusterer(classname="weka.clusterers.SimpleKMeans", options=["-N", str(self.clusters)])
self.clusterer.build_clusterer(data)
print(self.clusterer)
#Aplicamos la politica
self.politicaMax()
def readInstances(self):
#Direccion del fichero agente (instancias sin cabecera).
path = os.getcwd() + "/Outputs/agent.arff"
f = open(path, 'r')
index = 0
#Leemos cacda instancia
for line in f:
#Obtenemos los valores de los atributos (String)
values = line.split(",")
#Obtenemos el valor de la clase, de Norte a Oeste (0 - 3)
classValue = 0
classAtt = values[self.classIndex]
if (classAtt == "East"):
classValue = 1
elif (classAtt == "South"):
classValue = 2
elif (classAtt == "West"):
classValue = 3
#Obtenemos el valor del cluster.
cluster = values[self.clusterIndex]
#Incrementamos la cuenta de la clase para el cluster.
self.classCounts[int(cluster[-2:]) - 1][classValue] += 1
f.close()
#Calcula la clase mayoritaria para cada cluster
def politicaMax(self):
self.max = [0 for i in range(self.clusters)]
for i in range(self.clusters):
temp_max = 0
class_index = 0
for j in range(self.classes):
if (self.classCounts[i][j] > temp_max):
temp_max = self.classCounts[i][j]
class_index = j
self.max[i] = class_index
#print(class_index)
'''
for i in range(self.clusters):
print(self.max[i])
'''
def chooseAction(self, gameState):
path = os.getcwd() + "/Outputs/newInstance.arff"
f = open(path, 'w')
if (self.dis):
data = "@RELATION pacman\n" \
+ "@ATTRIBUTE dis NUMERIC\n" \
+ "@ATTRIBUTE relPos {-1,0,1,2,3,4,5,6,7,8}\n\n" \
+ "@DATA\n"
else:
data = "@RELATION pacman\n" \
+ "@ATTRIBUTE relPos {-1,0,1,2,3,4,5,6,7,8}\n\n" \
+ "@DATA\n"
# Obtenemos la posicion del pacman (x,y)
pos_pac = gameState.data.agentStates[0].getPosition()
# Obtenemos las distancias a los fantasmas
for i in range(1, gameState.getNumAgents()):
# Calculmos la distancia real (mazedistance) al fantasma i
pos_ghost = gameState.data.agentStates[i].getPosition()
distance = self.distancer.getDistance(pos_pac, pos_ghost)
#Normalizacion: (distance - min)/(max - min): min = 1, max = 21
distance = (distance - 1) / (21 - 1)
# Si la distancia es mayor a 1000 significa que el fantasma en cuestion ya ha sido comido
if (self.dis):
if (distance > 1000):
data = data + ("-1,")
else:
data = data + str(distance) + ","
# Obtenemos las posiciones relativas de los fantasmas con respecto del pacman
for i in range(1, gameState.getNumAgents()):
pos_ghost = gameState.data.agentStates[i].getPosition()
if (pos_ghost[1] < 3):
data = data + "-1,"
continue
# Si el fantasma esta en la misma posicion lo indicamos como 0
if (pos_ghost == pos_pac):
data = data + "0,"
# Determinamos las posiciones relativas
# {NORTH = 1, NORTH_EAST = 2, EAST = 3, SOUTH_EAST = 4, SOUTH = 5, SOUTH_WEST = 6, WEST = 7, NORTH_WEST = 8}.
if (pos_ghost[0] > pos_pac[0]):
if (pos_ghost[1] > pos_pac[1]):
data = data + "2,"
elif (pos_ghost[1] < pos_pac[1]):
data = data + "4,"
else:
data = data + "3,"
elif (pos_ghost[0] < pos_pac[0]):
if (pos_ghost[1] > pos_pac[1]):
data = data + "8,"
elif (pos_ghost[1] < pos_pac[1]):
data = data + "6,"
else:
data = data + "7,"
else:
if (pos_ghost[1] > pos_pac[1]):
data = data + "1,"
else:
data = data + "5,"
data = data + "\n"
#print(data)
f.write(data)
f.close()
loader = Loader(classname="weka.core.converters.ArffLoader")
newData = loader.load_file("/home/dot/Escritorio/Universidad/Machine Learning/practica 2/Outputs/newInstance.arff")
dir = 4
direction = Directions.STOP
for inst in newData:
cl = self.clusterer.cluster_instance(inst)
#print(cl)
dir = self.max[cl]
#print(dir)
if (dir == 0):
direction = Directions.NORTH
elif (dir == 1):
direction = Directions.EAST
elif (dir == 2):
direction = Directions.SOUTH
elif (dir == 3):
direction = Directions.WEST
#print(direction)
return direction
class GreedyBustersAgent(BustersAgent):
"An agent that charges the closest ghost."
def registerInitialState(self, gameState):
"Pre-computes the distance between every two points."
BustersAgent.registerInitialState(self, gameState)
self.distancer = Distancer(gameState.data.layout, False)
def chooseAction(self, gameState):
"""
First computes the most likely position of each ghost that has
not yet been captured, then chooses an action that brings
Pacman closer to the closest ghost (according to mazeDistance!).
To find the mazeDistance between any two positions, use:
self.distancer.getDistance(pos1, pos2)
To find the successor position of a position after an action:
successorPosition = Actions.getSuccessor(position, action)
livingGhostPositionDistributions, defined below, is a list of
util.Counter objects equal to the position belief
distributions for each of the ghosts that are still alive. It
is defined based on (these are implementation details about
which you need not be concerned):
1) gameState.getLivingGhosts(), a list of booleans, one for each
agent, indicating whether or not the agent is alive. Note
that pacman is always agent 0, so the ghosts are agents 1,
onwards (just as before).
2) self.ghostBeliefs, the list of belief distributions for each
of the ghosts (including ghosts that are not alive). The
indices into this list should be 1 less than indices into the
gameState.getLivingGhosts() list.
"""
pacmanPosition = gameState.getPacmanPosition()
legal = [a for a in gameState.getLegalPacmanActions()]
livingGhosts = gameState.getLivingGhosts()
livingGhostPositionDistributions = \
[beliefs for i, beliefs in enumerate(self.ghostBeliefs)
if livingGhosts[i+1]]
"Build a list of the positions with highest probability of having a ghost"
closestGhostPositions = []
for belief in livingGhostPositionDistributions:
closestGhostPositions.append(belief.argMax())
"Choose the position that has the highest probability of being the closest ghost"
closestGhostPosition = None
highestProbability = 0
for index, position in enumerate(closestGhostPositions):
prob = livingGhostPositionDistributions[index][position]
if prob >= highestProbability:
closestGhostPosition = position
highestProbability = prob
"Choose the action that creates the shortest distance to the closest ghost"
possibleActions = []
for action in legal:
successorPosition = Actions.getSuccessor(pacmanPosition, action)
distance = self.distancer.getDistance(successorPosition,
closestGhostPosition)
possibleActions.append((distance, action))
return min(possibleActions)[1]
def registerInitialState(self, gameState):
BustersAgent.registerInitialState(self, gameState)
self.distancer = Distancer(gameState.data.layout, False)
self.countActions = 0
#Obtenemos la direccion del fichero
#SameMaps -----------------------------------------------
path = os.getcwd() + "/Outputs/training_initial_v3_1A.arff"
#Abrimos el fichero
f = open(path,'a')
statInfo = os.stat(path)
if (statInfo.st_size == 0):
s = "@RELATION pacman\n\n" \
+ "@ATTRIBUTE pacx numeric\n" \
+ "@ATTRIBUTE pacy numeric\n" \
+ "@ATTRIBUTE legal_north {true, false}\n" \
+ "@ATTRIBUTE legal_east {true, false}\n" \
+ "@ATTRIBUTE legal_south {true, false}\n" \
+ "@ATTRIBUTE legal_west {true, false}\n" \
+ "@ATTRIBUTE g1_x NUMERIC\n" \
+ "@ATTRIBUTE g1_y NUMERIC\n" \
+ "@ATTRIBUTE g2_x NUMERIC\n" \
+ "@ATTRIBUTE g2_y NUMERIC\n" \
+ "@ATTRIBUTE g3_x NUMERIC\n" \
+ "@ATTRIBUTE g3_y NUMERIC\n" \
+ "@ATTRIBUTE g4_x NUMERIC\n" \
+ "@ATTRIBUTE g4_y NUMERIC\n" \
+ "@ATTRIBUTE g1_dis NUMERIC\n" \
+ "@ATTRIBUTE g2_dis NUMERIC\n" \
+ "@ATTRIBUTE g3_dis NUMERIC\n" \
+ "@ATTRIBUTE g4_dis NUMERIC\n" \
+ "@ATTRIBUTE num_walls NUMERIC\n" \
+ "@ATTRIBUTE alive_ghosts NUMERIC\n" \
+ "@ATTRIBUTE score NUMERIC\n" \
+ "@ATTRIBUTE future_score NUMERIC\n" \
+ "@ATTRIBUTE future_alive_ghosts NUMERIC\n" \
+ "@ATTRIBUTE last_action {Stop, North, East, South, West}\n" \
+ "@ATTRIBUTE g1_relPos {-1,0,1,2,3,4,5,6,7,8}\n" \
+ "@ATTRIBUTE g2_relPos {-1,0,1,2,3,4,5,6,7,8}\n" \
+ "@ATTRIBUTE g3_relPos {-1,0,1,2,3,4,5,6,7,8}\n" \
+ "@ATTRIBUTE g4_relPos {-1,0,1,2,3,4,5,6,7,8}\n" \
+ "@ATTRIBUTE g1_closest {true, false}\n" \
+ "@ATTRIBUTE g2_closest {true, false}\n" \
+ "@ATTRIBUTE g3_closest {true, false}\n" \
+ "@ATTRIBUTE g4_closest {true, false}\n" \
+ "@ATTRIBUTE north_best {true, false}\n" \
+ "@ATTRIBUTE east_best {true, false}\n" \
+ "@ATTRIBUTE south_best {true, false}\n" \
+ "@ATTRIBUTE west_best {true, false}\n" \
+ "@ATTRIBUTE action {North, East, South, West}\n\n" \
+ "@DATA\n"
s = "@RELATION pacman\n\n" \
+ "@ATTRIBUTE pacx numeric\n" \
+ "@ATTRIBUTE pacy numeric\n" \
+ "@ATTRIBUTE legal_north {true, false}\n" \
+ "@ATTRIBUTE legal_east {true, false}\n" \
+ "@ATTRIBUTE legal_south {true, false}\n" \
+ "@ATTRIBUTE legal_west {true, false}\n" \
+ "@ATTRIBUTE g1_x NUMERIC\n" \
+ "@ATTRIBUTE g1_y NUMERIC\n" \
+ "@ATTRIBUTE g1_dis NUMERIC\n" \
+ "@ATTRIBUTE num_walls NUMERIC\n" \
+ "@ATTRIBUTE alive_ghosts NUMERIC\n" \
+ "@ATTRIBUTE score NUMERIC\n" \
+ "@ATTRIBUTE future_score NUMERIC\n" \
+ "@ATTRIBUTE future_alive_ghosts NUMERIC\n" \
+ "@ATTRIBUTE last_action {Stop, North, East, South, West}\n" \
+ "@ATTRIBUTE g1_relPos {-1,0,1,2,3,4,5,6,7,8}\n" \
+ "@ATTRIBUTE g1_closest {true, false}\n" \
+ "@ATTRIBUTE north_best {true, false}\n" \
+ "@ATTRIBUTE east_best {true, false}\n" \
+ "@ATTRIBUTE south_best {true, false}\n" \
+ "@ATTRIBUTE west_best {true, false}\n" \
+ "@ATTRIBUTE action {North, East, South, West}\n\n" \
+ "@DATA\n"
f.write(s)
f.close()
class GreedyBustersAgent(BustersAgent):
"An agent that charges the closest ghost."
def registerInitialState(self, gameState):
"Pre-computes the distance between every two points."
BustersAgent.registerInitialState(self, gameState)
self.distancer = Distancer(gameState.data.layout, False)
def chooseAction(self, gameState):
"""
First computes the most likely position of each ghost that
has not yet been captured, then chooses an action that brings
Pacman closer to the closest ghost (in maze distance!).
To find the maze distance between any two positions, use:
self.distancer.getDistance(pos1, pos2)
To find the successor position of a position after an action:
successorPosition = Actions.getSuccessor(position, action)
livingGhostPositionDistributions, defined below, is a list of
util.Counter objects equal to the position belief distributions
for each of the ghosts that are still alive. It is defined based
on (these are implementation details about which you need not be
concerned):
1) gameState.getLivingGhosts(), a list of booleans, one for each
agent, indicating whether or not the agent is alive. Note
that pacman is always agent 0, so the ghosts are agents 1,
onwards (just as before).
2) self.ghostBeliefs, the list of belief distributions for each
of the ghosts (including ghosts that are not alive). The
indices into this list should be 1 less than indices into the
gameState.getLivingGhosts() list.
You may remove Directions.STOP from the list of available actions.
"""
pacmanPosition = gameState.getPacmanPosition()
legal = [
a for a in gameState.getLegalPacmanActions()
if a != Directions.STOP
]
livingGhosts = gameState.getLivingGhosts()
livingGhostPositionDistributions = [
beliefs for i, beliefs in enumerate(self.ghostBeliefs)
if livingGhosts[i + 1]
]
"*** YOUR CODE HERE ***"
"""
for x in range(0, 100):
try :
tempDist = self.distancer.getDistance(pacmanPosition, (x, 1))
except Exception:
print "Exception occurred at x = " + str(x)
for y in range(0, 100):
try :
tempDist = self.distancer.getDistance(pacmanPosition, (3, y))
except Exception:
print "Exception occurred at y = " + str(y)
"""
ghostLocs = util.Counter()
for agent in range(0, len(livingGhostPositionDistributions)):
#xcomList = [(0, 10000), (10, 5), (-20, 5)]
xcomList = [(pos[0], prob) for pos, prob in
livingGhostPositionDistributions[agent].iteritems()]
ycomList = [(pos[1], prob) for pos, prob in
livingGhostPositionDistributions[agent].iteritems()]
ghostLocs[agent] = self.clampOntoBoard(
(self.centerOfMass(xcomList), self.centerOfMass(ycomList)),
pacmanPosition)
ghostDist = 999999999999
closestAgent = 0
for agent in range(0, len(livingGhostPositionDistributions)):
tempDist = 99999999999
try:
tempDist = self.distancer.getDistance(pacmanPosition,
ghostLocs[agent])
except Exception:
print "Exception, not on board: " + str(
pacmanPosition) + ", " + str(ghostLocs[agent])
if (ghostDist >= tempDist):
ghostDist = tempDist
closestAgent = agent
bestDist = 999999999999
bestAct = legal[0]
for action in legal:
tempDist = 999999999
if ghostLocs[closestAgent] == (0, 0):
pass
else:
tempDist = self.distancer.getDistance(
ghostLocs[closestAgent],
self.castTuple(Actions.getSuccessor(
pacmanPosition, action)))
if tempDist < bestDist:
bestDist = tempDist
bestAct = action
return bestAct
# The input should be a tuple of (position, mass)
def centerOfMass(self, list):
sumOfProbs = 0
sumOfProbsXPos = 0
debugStr = ""
for pos, prob in list:
debugStr = debugStr + "(" + str(pos) + ", " + str(prob) + ")"
sumOfProbs = sumOfProbs + prob
sumOfProbsXPos = sumOfProbsXPos + (pos * prob)
if sumOfProbs == 0:
return 0
ret = int(round(sumOfProbsXPos / sumOfProbs))
return ret
def castTuple(self, tuple):
x = tuple[0]
y = tuple[1]
return (int(x), int(y))
# If the center of mass equation returns a point that is off the board, this equation will move it onto the board
def clampOntoBoard(self, point, pacmanPos):
# clamp the X
for delta in range(0, 10):
try:
self.distancer.getDistance(pacmanPos,
(point[0] - delta, point[1]))
return (point[0] - delta, point[1])
except Exception:
pass
try:
self.distancer.getDistance(pacmanPos,
(point[0] + delta, point[1]))
return (point[0] + delta, point[1])
except Exception:
pass
try:
self.distancer.getDistance(pacmanPos,
(point[0], point[1] + delta))
return (point[0], point[1] + delta)
except Exception:
pass
try:
self.distancer.getDistance(pacmanPos,
(point[0], point[1] - delta))
return (point[0], point[1] - delta)
except Exception:
pass
delta = delta + 1
print "Unable to find a clamp for pos " + str(point)
return (0, 0)
class GreedyBustersAgent(BustersAgent):
"An agent that charges the closest ghost."
def registerInitialState(self, gameState):
"Pre-computes the distance between every two points."
BustersAgent.registerInitialState(self, gameState)
self.distancer = Distancer(gameState.data.layout, False)
def chooseAction(self, gameState):
"""
First computes the most likely position of each ghost that has
not yet been captured, then chooses an action that brings
Pacman closer to the closest ghost (according to mazeDistance!).
To find the mazeDistance between any two positions, use:
self.distancer.getDistance(pos1, pos2)
To find the successor position of a position after an action:
successorPosition = Actions.getSuccessor(position, action)
livingGhostPositionDistributions, defined below, is a list of
util.Counter objects equal to the position belief
distributions for each of the ghosts that are still alive. It
is defined based on (these are implementation details about
which you need not be concerned):
1) gameState.getLivingGhosts(), a list of booleans, one for each
agent, indicating whether or not the agent is alive. Note
that pacman is always agent 0, so the ghosts are agents 1,
onwards (just as before).
2) self.ghostBeliefs, the list of belief distributions for each
of the ghosts (including ghosts that are not alive). The
indices into this list should be 1 less than indices into the
gameState.getLivingGhosts() list.
"""
# borrowed from https://stackoverflow.com/questions/268272/getting-key-with-maximum-value-in-dictionary
def keywithmaxval(dict):
v = list(dict.values())
k = list(dict.keys())
return k[v.index(max(v))]
def keywithminval(dict):
v = list(dict.values())
k = list(dict.keys())
return k[v.index(min(v))]
pacmanPosition = gameState.getPacmanPosition()
legal = [a for a in gameState.getLegalPacmanActions()]
livingGhosts = gameState.getLivingGhosts()
livingGhostPositionDistributions = \
[beliefs for i, beliefs in enumerate(self.ghostBeliefs)
if livingGhosts[i+1]]
most_likely_positions = {}
for i in range(len(livingGhostPositionDistributions)):
most_likely_positions[i] = keywithmaxval(livingGhostPositionDistributions[i])
closest_ghost = keywithmaxval(most_likely_positions)
closest_pos = most_likely_positions[closest_ghost]
distances = {}
for a in legal:
succPos = Actions.getSuccessor(pacmanPosition, a)
distances[a] = self.distancer.getDistance(succPos, closest_pos)
return keywithminval(distances)
def registerInitialState(self, gameState):
BustersAgent.registerInitialState(self, gameState)
self.distancer = Distancer(gameState.data.layout, False)
self.countActions = 0
def registerInitialState(self, gameState):
"""Initializes some helper modules"""
import __main__
self.display = __main__._display
self.distancer = Distancer(gameState.data.layout, False)
self.firstMove = True
class GreedyBustersAgent(BustersAgent):
"An agent that charges the closest ghost."
def registerInitialState(self, gameState):
"Pre-computes the distance between every two points."
BustersAgent.registerInitialState(self, gameState)
self.distancer = Distancer(gameState.data.layout, False)
def chooseAction(self, gameState):
"""
First computes the most likely position of each ghost that has
not yet been captured, then chooses an action that brings
Pacman closer to the closest ghost (according to mazeDistance!).
To find the mazeDistance between any two positions, use:
self.distancer.getDistance(pos1, pos2)
To find the successor position of a position after an action:
successorPosition = Actions.getSuccessor(position, action)
livingGhostPositionDistributions, defined below, is a list of
util.Counter objects equal to the position belief
distributions for each of the ghosts that are still alive. It
is defined based on (these are implementation details about
which you need not be concerned):
1) gameState.getLivingGhosts(), a list of booleans, one for each
agent, indicating whether or not the agent is alive. Note
that pacman is always agent 0, so the ghosts are agents 1,
onwards (just as before).
2) self.ghostBeliefs, the list of belief distributions for each
of the ghosts (including ghosts that are not alive). The
indices into this list should be 1 less than indices into the
gameState.getLivingGhosts() list.
"""
pacmanPosition = gameState.getPacmanPosition()
legal = [a for a in gameState.getLegalPacmanActions()]
livingGhosts = gameState.getLivingGhosts()
livingGhostPosDist = \
[beliefs for i, beliefs in enumerate(self.ghostBeliefs)
if livingGhosts[i+1]]
"*** YOUR CODE HERE ***"
#for each ghost find the position with the maxiumum likelyhood of the ghost being there
#then find the distance from pacman to that spot
closest_dist = float('inf')
closest_ghost = None
for i in range(len(livingGhostPosDist)):
max_val = max(livingGhostPosDist[i].values())
likelyPos = max(livingGhostPosDist[i],
key=livingGhostPosDist[i].get)
#print likelyPos
distToPac = self.distancer.getDistance(pacmanPosition, likelyPos)
if distToPac < closest_dist:
closest_dist = distToPac
closest_ghost = likelyPos
if closest_ghost == None:
print "FRICK"
return None
minDistMove = float('inf')
chosenMove = None
for act in legal:
successorPosition = Actions.getSuccessor(pacmanPosition, act)
distToGhost = self.distancer.getDistance(closest_ghost,
successorPosition)
if distToGhost < minDistMove:
minDistMove = distToGhost
chosenMove = act
return chosenMove