forked from davislf2/AI_Pacman
-
Notifications
You must be signed in to change notification settings - Fork 0
/
capture.py
1028 lines (867 loc) · 36.3 KB
/
capture.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
# capture.py
# ----------
# Licensing Information: You are free to use or extend these projects for
# educational purposes provided that (1) you do not distribute or publish
# solutions, (2) you retain this notice, and (3) you provide clear
# attribution to UC Berkeley, including a link to https://ai.berkeley.edu.
#
# Attribution Information: The Pacman AI projects were developed at UC Berkeley.
# The core projects and autograders were primarily created by John DeNero
# ([email protected]) and Dan Klein ([email protected]).
# Student side autograding was added by Brad Miller, Nick Hay, and
# Pieter Abbeel ([email protected]).
# capture.py
# ----------
# Licensing Information: Please do not distribute or publish solutions to this
# project. You are free to use and extend these projects for educational
# purposes. The Pacman AI projects were developed at UC Berkeley, primarily by
# John DeNero ([email protected]) and Dan Klein ([email protected]).
# For more info, see https://inst.eecs.berkeley.edu/~cs188/sp09/pacman.html
"""
Capture.py holds the logic for Pacman capture the flag.
(i) Your interface to the pacman world:
Pacman is a complex environment. You probably don't want to
read through all of the code we wrote to make the game runs
correctly. This section contains the parts of the code
that you will need to understand in order to complete the
project. There is also some code in game.py that you should
understand.
(ii) The hidden secrets of pacman:
This section contains all of the logic code that the pacman
environment uses to decide who can move where, who dies when
things collide, etc. You shouldn't need to read this section
of code, but you can if you want.
(iii) Framework to start a game:
The final section contains the code for reading the command
you use to set up the game, then starting up a new game, along with
linking in all the external parts (agent functions, graphics).
Check this section out to see all the options available to you.
To play your first game, type 'python capture.py' from the command line.
The keys are
P1: 'a', 's', 'd', and 'w' to move
P2: 'l', ';', ',' and 'p' to move
"""
from game import GameStateData
from game import Game
from game import Directions
from game import Actions
from util import nearestPoint
from util import manhattanDistance
from game import Grid
from game import Configuration
from game import Agent
from game import reconstituteGrid
import sys, util, types, time, random, imp
import keyboardAgents
# If you change these, you won't affect the server, so you can't cheat
KILL_POINTS = 0
SONAR_NOISE_RANGE = 13 # Must be odd
SONAR_NOISE_VALUES = [i - (SONAR_NOISE_RANGE - 1)/2 for i in range(SONAR_NOISE_RANGE)]
SIGHT_RANGE = 5 # Manhattan distance
MIN_FOOD = 2
TOTAL_FOOD = 60
DUMP_FOOD_ON_DEATH = True # if we have the gameplay element that dumps dots on death
SCARED_TIME = 40
def noisyDistance(pos1, pos2):
return int(util.manhattanDistance(pos1, pos2) + random.choice(SONAR_NOISE_VALUES))
###################################################
# YOUR INTERFACE TO THE PACMAN WORLD: A GameState #
###################################################
class GameState:
"""
A GameState specifies the full game state, including the food, capsules,
agent configurations and score changes.
GameStates are used by the Game object to capture the actual state of the game and
can be used by agents to reason about the game.
Much of the information in a GameState is stored in a GameStateData object. We
strongly suggest that you access that data via the accessor methods below rather
than referring to the GameStateData object directly.
"""
####################################################
# Accessor methods: use these to access state data #
####################################################
def getLegalActions( self, agentIndex=0 ):
"""
Returns the legal actions for the agent specified.
"""
return AgentRules.getLegalActions( self, agentIndex )
def generateSuccessor( self, agentIndex, action):
"""
Returns the successor state (a GameState object) after the specified agent takes the action.
"""
# Copy current state
state = GameState(self)
# Find appropriate rules for the agent
AgentRules.applyAction( state, action, agentIndex )
AgentRules.checkDeath(state, agentIndex)
AgentRules.decrementTimer(state.data.agentStates[agentIndex])
# Book keeping
state.data._agentMoved = agentIndex
state.data.score += state.data.scoreChange
state.data.timeleft = self.data.timeleft - 1
return state
def getAgentState(self, index):
return self.data.agentStates[index]
def getAgentPosition(self, index):
"""
Returns a location tuple if the agent with the given index is observable;
if the agent is unobservable, returns None.
"""
agentState = self.data.agentStates[index]
ret = agentState.getPosition()
if ret:
return tuple(int(x) for x in ret)
return ret
def getNumAgents( self ):
return len( self.data.agentStates )
def getScore( self ):
"""
Returns a number corresponding to the current score.
"""
return self.data.score
def getRedFood(self):
"""
Returns a matrix of food that corresponds to the food on the red team's side.
For the matrix m, m[x][y]=true if there is food in (x,y) that belongs to
red (meaning red is protecting it, blue is trying to eat it).
"""
return halfGrid(self.data.food, red = True)
def getBlueFood(self):
"""
Returns a matrix of food that corresponds to the food on the blue team's side.
For the matrix m, m[x][y]=true if there is food in (x,y) that belongs to
blue (meaning blue is protecting it, red is trying to eat it).
"""
return halfGrid(self.data.food, red = False)
def getRedCapsules(self):
return halfList(self.data.capsules, self.data.food, red = True)
def getBlueCapsules(self):
return halfList(self.data.capsules, self.data.food, red = False)
def getWalls(self):
"""
Just like getFood but for walls
"""
return self.data.layout.walls
def hasFood(self, x, y):
"""
Returns true if the location (x,y) has food, regardless of
whether it's blue team food or red team food.
"""
return self.data.food[x][y]
def hasWall(self, x, y):
"""
Returns true if (x,y) has a wall, false otherwise.
"""
return self.data.layout.walls[x][y]
def isOver( self ):
return self.data._win
def getRedTeamIndices(self):
"""
Returns a list of agent index numbers for the agents on the red team.
"""
return self.redTeam[:]
def getBlueTeamIndices(self):
"""
Returns a list of the agent index numbers for the agents on the blue team.
"""
return self.blueTeam[:]
def isOnRedTeam(self, agentIndex):
"""
Returns true if the agent with the given agentIndex is on the red team.
"""
return self.teams[agentIndex]
def getAgentDistances(self):
"""
Returns a noisy distance to each agent.
"""
if 'agentDistances' in dir(self) :
return self.agentDistances
else:
return None
def getDistanceProb(self, trueDistance, noisyDistance):
"Returns the probability of a noisy distance given the true distance"
if noisyDistance - trueDistance in SONAR_NOISE_VALUES:
return 1.0/SONAR_NOISE_RANGE
else:
return 0
def getInitialAgentPosition(self, agentIndex):
"Returns the initial position of an agent."
return self.data.layout.agentPositions[agentIndex][1]
def getCapsules(self):
"""
Returns a list of positions (x,y) of the remaining capsules.
"""
return self.data.capsules
#############################################
# Helper methods: #
# You shouldn't need to call these directly #
#############################################
def __init__( self, prevState = None ):
"""
Generates a new state by copying information from its predecessor.
"""
if prevState != None: # Initial state
self.data = GameStateData(prevState.data)
self.blueTeam = prevState.blueTeam
self.redTeam = prevState.redTeam
self.data.timeleft = prevState.data.timeleft
self.teams = prevState.teams
self.agentDistances = prevState.agentDistances
else:
self.data = GameStateData()
self.agentDistances = []
def deepCopy( self ):
state = GameState( self )
state.data = self.data.deepCopy()
state.data.timeleft = self.data.timeleft
state.blueTeam = self.blueTeam[:]
state.redTeam = self.redTeam[:]
state.teams = self.teams[:]
state.agentDistances = self.agentDistances[:]
return state
def makeObservation(self, index):
state = self.deepCopy()
# Adds the sonar signal
pos = state.getAgentPosition(index)
n = state.getNumAgents()
distances = [noisyDistance(pos, state.getAgentPosition(i)) for i in range(n)]
state.agentDistances = distances
# Remove states of distant opponents
if index in self.blueTeam:
team = self.blueTeam
otherTeam = self.redTeam
else:
otherTeam = self.blueTeam
team = self.redTeam
for enemy in otherTeam:
seen = False
enemyPos = state.getAgentPosition(enemy)
for teammate in team:
if util.manhattanDistance(enemyPos, state.getAgentPosition(teammate)) <= SIGHT_RANGE:
seen = True
if not seen: state.data.agentStates[enemy].configuration = None
return state
def __eq__( self, other ):
"""
Allows two states to be compared.
"""
if other == None: return False
return self.data == other.data
def __hash__( self ):
"""
Allows states to be keys of dictionaries.
"""
return int(hash( self.data ))
def __str__( self ):
return str(self.data)
def initialize( self, layout, numAgents):
"""
Creates an initial game state from a layout array (see layout.py).
"""
self.data.initialize(layout, numAgents)
positions = [a.configuration for a in self.data.agentStates]
self.blueTeam = [i for i,p in enumerate(positions) if not self.isRed(p)]
self.redTeam = [i for i,p in enumerate(positions) if self.isRed(p)]
self.teams = [self.isRed(p) for p in positions]
#This is usually 60 (always 60 with random maps)
#However, if layout map is specified otherwise, it could be less
global TOTAL_FOOD
TOTAL_FOOD = layout.totalFood
def isRed(self, configOrPos):
width = self.data.layout.width
if type(configOrPos) == type( (0,0) ):
return configOrPos[0] < width / 2
else:
return configOrPos.pos[0] < width / 2
def halfGrid(grid, red):
halfway = grid.width / 2
halfgrid = Grid(grid.width, grid.height, False)
if red: xrange = range(halfway)
else: xrange = range(halfway, grid.width)
for y in range(grid.height):
for x in xrange:
if grid[x][y]: halfgrid[x][y] = True
return halfgrid
def halfList(l, grid, red):
halfway = grid.width / 2
newList = []
for x,y in l:
if red and x <= halfway: newList.append((x,y))
elif not red and x > halfway: newList.append((x,y))
return newList
############################################################################
# THE HIDDEN SECRETS OF PACMAN #
# #
# You shouldn't need to look through the code in this section of the file. #
############################################################################
COLLISION_TOLERANCE = 0.7 # How close ghosts must be to Pacman to kill
class CaptureRules:
"""
These game rules manage the control flow of a game, deciding when
and how the game starts and ends.
"""
def __init__(self, quiet = False):
self.quiet = quiet
def newGame( self, layout, agents, display, length, muteAgents, catchExceptions ):
initState = GameState()
initState.initialize( layout, len(agents) )
starter = random.randint(0,1)
print('%s team starts' % ['Red', 'Blue'][starter])
game = Game(agents, display, self, startingIndex=starter, muteAgents=muteAgents, catchExceptions=catchExceptions)
game.state = initState
game.length = length
game.state.data.timeleft = length
if 'drawCenterLine' in dir(display):
display.drawCenterLine()
self._initBlueFood = initState.getBlueFood().count()
self._initRedFood = initState.getRedFood().count()
return game
def process(self, state, game):
"""
Checks to see whether it is time to end the game.
"""
if 'moveHistory' in dir(game):
if len(game.moveHistory) == game.length:
state.data._win = True
if state.isOver():
game.gameOver = True
if not game.rules.quiet:
redCount = 0
blueCount = 0
foodToWin = (TOTAL_FOOD/2) - MIN_FOOD
for index in range(state.getNumAgents()):
agentState = state.data.agentStates[index]
if index in state.getRedTeamIndices():
redCount += agentState.numReturned
else:
blueCount += agentState.numReturned
if blueCount >= foodToWin:#state.getRedFood().count() == MIN_FOOD:
print 'The Blue team has returned at least %d of the opponents\' dots.' % foodToWin
elif redCount >= foodToWin:#state.getBlueFood().count() == MIN_FOOD:
print 'The Red team has returned at least %d of the opponents\' dots.' % foodToWin
else:#if state.getBlueFood().count() > MIN_FOOD and state.getRedFood().count() > MIN_FOOD:
print 'Time is up.'
if state.data.score == 0: print 'Tie game!'
else:
winner = 'Red'
if state.data.score < 0: winner = 'Blue'
print 'The %s team wins by %d points.' % (winner, abs(state.data.score))
def getProgress(self, game):
blue = 1.0 - (game.state.getBlueFood().count() / float(self._initBlueFood))
red = 1.0 - (game.state.getRedFood().count() / float(self._initRedFood))
moves = len(self.moveHistory) / float(game.length)
# return the most likely progress indicator, clamped to [0, 1]
return min(max(0.75 * max(red, blue) + 0.25 * moves, 0.0), 1.0)
def agentCrash(self, game, agentIndex):
if agentIndex % 2 == 0:
print >>sys.stderr, "Red agent crashed"
game.state.data.score = -1
else:
print >>sys.stderr, "Blue agent crashed"
game.state.data.score = 1
def getMaxTotalTime(self, agentIndex):
return 900 # Move limits should prevent this from ever happening
def getMaxStartupTime(self, agentIndex):
return 15 # 15 seconds for registerInitialState
def getMoveWarningTime(self, agentIndex):
return 1 # One second per move
def getMoveTimeout(self, agentIndex):
return 3 # Three seconds results in instant forfeit
def getMaxTimeWarnings(self, agentIndex):
return 2 # Third violation loses the game
class AgentRules:
"""
These functions govern how each agent interacts with her environment.
"""
def getLegalActions( state, agentIndex ):
"""
Returns a list of legal actions (which are both possible & allowed)
"""
agentState = state.getAgentState(agentIndex)
conf = agentState.configuration
possibleActions = Actions.getPossibleActions( conf, state.data.layout.walls )
return AgentRules.filterForAllowedActions( agentState, possibleActions)
getLegalActions = staticmethod( getLegalActions )
def filterForAllowedActions(agentState, possibleActions):
return possibleActions
filterForAllowedActions = staticmethod( filterForAllowedActions )
def applyAction( state, action, agentIndex ):
"""
Edits the state to reflect the results of the action.
"""
legal = AgentRules.getLegalActions( state, agentIndex )
if action not in legal:
raise Exception("Illegal action " + str(action))
# Update Configuration
agentState = state.data.agentStates[agentIndex]
speed = 1.0
# if agentState.isPacman: speed = 0.5
vector = Actions.directionToVector( action, speed )
oldConfig = agentState.configuration
agentState.configuration = oldConfig.generateSuccessor( vector )
# Eat
next = agentState.configuration.getPosition()
nearest = nearestPoint( next )
if next == nearest:
isRed = state.isOnRedTeam(agentIndex)
# Change agent type
agentState.isPacman = [isRed, state.isRed(agentState.configuration)].count(True) == 1
# if he's no longer pacman, he's on his own side, so reset the num carrying timer
#agentState.numCarrying *= int(agentState.isPacman)
if agentState.numCarrying > 0 and not agentState.isPacman:
score = agentState.numCarrying if isRed else -1*agentState.numCarrying
state.data.scoreChange += score
agentState.numReturned += agentState.numCarrying
agentState.numCarrying = 0
redCount = 0
blueCount = 0
for index in range(state.getNumAgents()):
agentState = state.data.agentStates[index]
if index in state.getRedTeamIndices():
redCount += agentState.numReturned
else:
blueCount += agentState.numReturned
if redCount >= (TOTAL_FOOD/2) - MIN_FOOD or blueCount >= (TOTAL_FOOD/2) - MIN_FOOD:
state.data._win = True
if agentState.isPacman and manhattanDistance( nearest, next ) <= 0.9 :
AgentRules.consume( nearest, state, state.isOnRedTeam(agentIndex) )
applyAction = staticmethod( applyAction )
def consume( position, state, isRed ):
x,y = position
# Eat food
if state.data.food[x][y]:
# blue case is the default
teamIndicesFunc = state.getBlueTeamIndices
score = -1
if isRed:
# switch if its red
score = 1
teamIndicesFunc = state.getRedTeamIndices
# go increase the variable for the pacman who ate this
agents = [state.data.agentStates[agentIndex] for agentIndex in teamIndicesFunc()]
for agent in agents:
if agent.getPosition() == position:
agent.numCarrying += 1
break # the above should only be true for one agent...
# do all the score and food grid maintainenace
#state.data.scoreChange += score
state.data.food = state.data.food.copy()
state.data.food[x][y] = False
state.data._foodEaten = position
#if (isRed and state.getBlueFood().count() == MIN_FOOD) or (not isRed and state.getRedFood().count() == MIN_FOOD):
# state.data._win = True
# Eat capsule
if isRed: myCapsules = state.getBlueCapsules()
else: myCapsules = state.getRedCapsules()
if( position in myCapsules ):
state.data.capsules.remove( position )
state.data._capsuleEaten = position
# Reset all ghosts' scared timers
if isRed: otherTeam = state.getBlueTeamIndices()
else: otherTeam = state.getRedTeamIndices()
for index in otherTeam:
state.data.agentStates[index].scaredTimer = SCARED_TIME
consume = staticmethod( consume )
def decrementTimer(state):
timer = state.scaredTimer
if timer == 1:
state.configuration.pos = nearestPoint( state.configuration.pos )
state.scaredTimer = max( 0, timer - 1 )
decrementTimer = staticmethod( decrementTimer )
def dumpFoodFromDeath(state, agentState, agentIndex):
if not (DUMP_FOOD_ON_DEATH):
# this feature is not turned on
return
if not agentState.isPacman:
raise Exception('something is seriously wrong, this agent isnt a pacman!')
# ok so agentState is this:
if (agentState.numCarrying == 0):
return
# first, score changes!
# we HACK pack that ugly bug by just determining if its red based on the first position
# to die...
dummyConfig = Configuration(agentState.getPosition(), 'North')
isRed = state.isRed(dummyConfig)
# the score increases if red eats dots, so if we are refunding points,
# the direction should be -1 if the red agent died, which means he dies
# on the blue side
scoreDirection = (-1)**(int(isRed) + 1)
#state.data.scoreChange += scoreDirection * agentState.numCarrying
def onRightSide(state, x, y):
dummyConfig = Configuration((x, y), 'North')
return state.isRed(dummyConfig) == isRed
# we have food to dump
# -- expand out in BFS. Check:
# - that it's within the limits
# - that it's not a wall
# - that no other agents are there
# - that no power pellets are there
# - that it's on the right side of the grid
def allGood(state, x, y):
width, height = state.data.layout.width, state.data.layout.height
food, walls = state.data.food, state.data.layout.walls
# bounds check
if x >= width or y >= height or x <= 0 or y <= 0:
return False
if walls[x][y]:
return False
if food[x][y]:
return False
# dots need to be on the side where this agent will be a pacman :P
if not onRightSide(state, x, y):
return False
if (x,y) in state.data.capsules:
return False
# loop through agents
agentPoses = [state.getAgentPosition(i) for i in range(state.getNumAgents())]
if (x,y) in agentPoses:
return False
return True
numToDump = agentState.numCarrying
state.data.food = state.data.food.copy()
foodAdded = []
def genSuccessors(x, y):
DX = [-1, 0, 1]
DY = [-1, 0, 1]
return [(x + dx, y + dy) for dx in DX for dy in DY]
# BFS graph search
positionQueue = [agentState.getPosition()]
seen = set()
while numToDump > 0:
if not len(positionQueue):
raise Exception('Exhausted BFS! uh oh')
# pop one off, graph check
popped = positionQueue.pop(0)
if popped in seen:
continue
seen.add(popped)
x, y = popped[0], popped[1]
x = int(x)
y = int(y)
if (allGood(state, x, y)):
state.data.food[x][y] = True
foodAdded.append((x, y))
numToDump -= 1
# generate successors
positionQueue = positionQueue + genSuccessors(x, y)
state.data._foodAdded = foodAdded
# now our agentState is no longer carrying food
agentState.numCarrying = 0
pass
dumpFoodFromDeath = staticmethod(dumpFoodFromDeath)
def checkDeath( state, agentIndex):
agentState = state.data.agentStates[agentIndex]
if state.isOnRedTeam(agentIndex):
otherTeam = state.getBlueTeamIndices()
else:
otherTeam = state.getRedTeamIndices()
if agentState.isPacman:
for index in otherTeam:
otherAgentState = state.data.agentStates[index]
if otherAgentState.isPacman: continue
ghostPosition = otherAgentState.getPosition()
if ghostPosition == None: continue
if manhattanDistance( ghostPosition, agentState.getPosition() ) <= COLLISION_TOLERANCE:
# award points to the other team for killing Pacmen
if otherAgentState.scaredTimer <= 0:
AgentRules.dumpFoodFromDeath(state, agentState, agentIndex)
score = KILL_POINTS
if state.isOnRedTeam(agentIndex):
score = -score
state.data.scoreChange += score
agentState.isPacman = False
agentState.configuration = agentState.start
agentState.scaredTimer = 0
else:
score = KILL_POINTS
if state.isOnRedTeam(agentIndex):
score = -score
state.data.scoreChange += score
otherAgentState.isPacman = False
otherAgentState.configuration = otherAgentState.start
otherAgentState.scaredTimer = 0
else: # Agent is a ghost
for index in otherTeam:
otherAgentState = state.data.agentStates[index]
if not otherAgentState.isPacman: continue
pacPos = otherAgentState.getPosition()
if pacPos == None: continue
if manhattanDistance( pacPos, agentState.getPosition() ) <= COLLISION_TOLERANCE:
#award points to the other team for killing Pacmen
if agentState.scaredTimer <= 0:
AgentRules.dumpFoodFromDeath(state, otherAgentState, agentIndex)
score = KILL_POINTS
if not state.isOnRedTeam(agentIndex):
score = -score
state.data.scoreChange += score
otherAgentState.isPacman = False
otherAgentState.configuration = otherAgentState.start
otherAgentState.scaredTimer = 0
else:
score = KILL_POINTS
if state.isOnRedTeam(agentIndex):
score = -score
state.data.scoreChange += score
agentState.isPacman = False
agentState.configuration = agentState.start
agentState.scaredTimer = 0
checkDeath = staticmethod( checkDeath )
def placeGhost(state, ghostState):
ghostState.configuration = ghostState.start
placeGhost = staticmethod( placeGhost )
#############################
# FRAMEWORK TO START A GAME #
#############################
def default(str):
return str + ' [Default: %default]'
def parseAgentArgs(str):
if str == None or str == '': return {}
pieces = str.split(',')
opts = {}
for p in pieces:
if '=' in p:
key, val = p.split('=')
else:
key,val = p, 1
opts[key] = val
return opts
def readCommand( argv ):
"""
Processes the command used to run pacman from the command line.
"""
from optparse import OptionParser
usageStr = """
USAGE: python pacman.py <options>
EXAMPLES: (1) python capture.py
- starts a game with two baseline agents
(2) python capture.py --keys0
- starts a two-player interactive game where the arrow keys control agent 0, and all other agents are baseline agents
(3) python capture.py -r baselineTeam -b myTeam
- starts a fully automated game where the red team is a baseline team and blue team is myTeam
"""
parser = OptionParser(usageStr)
parser.add_option('-r', '--red', help=default('Red team'),
default='baselineTeam')
parser.add_option('-b', '--blue', help=default('Blue team'),
default='baselineTeam')
parser.add_option('--red-name', help=default('Red team name'),
default='Red')
parser.add_option('--blue-name', help=default('Blue team name'),
default='Blue')
parser.add_option('--redOpts', help=default('Options for red team (e.g. first=keys)'),
default='')
parser.add_option('--blueOpts', help=default('Options for blue team (e.g. first=keys)'),
default='')
parser.add_option('--keys0', help='Make agent 0 (first red player) a keyboard agent', action='store_true',default=False)
parser.add_option('--keys1', help='Make agent 1 (second red player) a keyboard agent', action='store_true',default=False)
parser.add_option('--keys2', help='Make agent 2 (first blue player) a keyboard agent', action='store_true',default=False)
parser.add_option('--keys3', help='Make agent 3 (second blue player) a keyboard agent', action='store_true',default=False)
parser.add_option('-l', '--layout', dest='layout',
help=default('the LAYOUT_FILE from which to load the map layout; use RANDOM for a random maze; use RANDOM<seed> to use a specified random seed, e.g., RANDOM23'),
metavar='LAYOUT_FILE', default='defaultCapture')
parser.add_option('-t', '--textgraphics', action='store_true', dest='textgraphics',
help='Display output as text only', default=False)
parser.add_option('-q', '--quiet', action='store_true',
help='Display minimal output and no graphics', default=False)
parser.add_option('-Q', '--super-quiet', action='store_true', dest="super_quiet",
help='Same as -q but agent output is also suppressed', default=False)
parser.add_option('-z', '--zoom', type='float', dest='zoom',
help=default('Zoom in the graphics'), default=1)
parser.add_option('-i', '--time', type='int', dest='time',
help=default('TIME limit of a game in moves'), default=1200, metavar='TIME')
parser.add_option('-n', '--numGames', type='int',
help=default('Number of games to play'), default=1)
parser.add_option('-f', '--fixRandomSeed', action='store_true',
help='Fixes the random seed to always play the same game', default=False)
parser.add_option('--record', action='store_true',
help='Writes game histories to a file (named by the time they were played)', default=False)
parser.add_option('--replay', default=None,
help='Replays a recorded game file.')
parser.add_option('-x', '--numTraining', dest='numTraining', type='int',
help=default('How many episodes are training (suppresses output)'), default=0)
parser.add_option('-c', '--catchExceptions', action='store_true', default=False,
help='Catch exceptions and enforce time limits')
options, otherjunk = parser.parse_args(argv)
assert len(otherjunk) == 0, "Unrecognized options: " + str(otherjunk)
args = dict()
# Choose a display format
#if options.pygame:
# import pygameDisplay
# args['display'] = pygameDisplay.PacmanGraphics()
if options.textgraphics:
import textDisplay
args['display'] = textDisplay.PacmanGraphics()
elif options.quiet:
import textDisplay
args['display'] = textDisplay.NullGraphics()
elif options.super_quiet:
import textDisplay
args['display'] = textDisplay.NullGraphics()
args['muteAgents'] = True
else:
import captureGraphicsDisplay
# Hack for agents writing to the display
captureGraphicsDisplay.FRAME_TIME = 0
args['display'] = captureGraphicsDisplay.PacmanGraphics(options.red, options.blue, options.zoom, 0, capture=True)
import __main__
__main__.__dict__['_display'] = args['display']
args['redTeamName'] = options.red_name
args['blueTeamName'] = options.blue_name
if options.fixRandomSeed: random.seed('cs188')
# Special case: recorded games don't use the runGames method or args structure
if options.replay != None:
print 'Replaying recorded game %s.' % options.replay
import cPickle
recorded = cPickle.load(open(options.replay))
recorded['display'] = args['display']
replayGame(**recorded)
sys.exit(0)
# Choose a pacman agent
redArgs, blueArgs = parseAgentArgs(options.redOpts), parseAgentArgs(options.blueOpts)
if options.numTraining > 0:
redArgs['numTraining'] = options.numTraining
blueArgs['numTraining'] = options.numTraining
nokeyboard = options.textgraphics or options.quiet or options.numTraining > 0
print '\nRed team %s with %s:' % (options.red, redArgs)
redAgents = loadAgents(True, options.red, nokeyboard, redArgs)
print '\nBlue team %s with %s:' % (options.blue, blueArgs)
blueAgents = loadAgents(False, options.blue, nokeyboard, blueArgs)
args['agents'] = sum([list(el) for el in zip(redAgents, blueAgents)],[]) # list of agents
numKeyboardAgents = 0
for index, val in enumerate([options.keys0, options.keys1, options.keys2, options.keys3]):
if not val: continue
if numKeyboardAgents == 0:
agent = keyboardAgents.KeyboardAgent(index)
elif numKeyboardAgents == 1:
agent = keyboardAgents.KeyboardAgent2(index)
else:
raise Exception('Max of two keyboard agents supported')
numKeyboardAgents += 1
args['agents'][index] = agent
# Choose a layout
import layout
layouts = []
for i in range(options.numGames):
if options.layout == 'RANDOM':
l = layout.Layout(randomLayout().split('\n'))
elif options.layout.startswith('RANDOM'):
l = layout.Layout(randomLayout(int(options.layout[6:])).split('\n'))
elif options.layout.lower().find('capture') == -1:
raise Exception( 'You must use a capture layout with capture.py')
else:
l = layout.getLayout( options.layout )
if l == None: raise Exception("The layout " + options.layout + " cannot be found")
layouts.append(l)
args['layouts'] = layouts
args['length'] = options.time
args['numGames'] = options.numGames
args['numTraining'] = options.numTraining
args['record'] = options.record
args['catchExceptions'] = options.catchExceptions
return args
def randomLayout(seed = None):
if not seed:
seed = random.randint(0,99999999)
# layout = 'layouts/random%08dCapture.lay' % seed
# print 'Generating random layout in %s' % layout
import mazeGenerator
return mazeGenerator.generateMaze(seed)
import traceback
def loadAgents(isRed, factory, textgraphics, cmdLineArgs):
"Calls agent factories and returns lists of agents"
try:
if not factory.endswith(".py"):
factory += ".py"
module = imp.load_source('player' + str(int(isRed)), factory)
except (NameError, ImportError):
print >>sys.stderr, 'Error: The team "' + factory + '" could not be loaded! '
traceback.print_exc()
return [None for i in range(2)]
args = dict()
args.update(cmdLineArgs) # Add command line args with priority
print "Loading Team:", factory
print "Arguments:", args
# if textgraphics and factoryClassName.startswith('Keyboard'):
# raise Exception('Using the keyboard requires graphics (no text display, quiet or training games)')
try:
createTeamFunc = getattr(module, 'createTeam')
except AttributeError:
print >>sys.stderr, 'Error: The team "' + factory + '" could not be loaded! '
traceback.print_exc()
return [None for i in range(2)]
indexAddend = 0
if not isRed:
indexAddend = 1
indices = [2*i + indexAddend for i in range(2)]
return createTeamFunc(indices[0], indices[1], isRed, **args)
def replayGame( layout, agents, actions, display, length, redTeamName, blueTeamName ):
rules = CaptureRules()
game = rules.newGame( layout, agents, display, length, False, False )
state = game.state
display.redTeam = redTeamName
display.blueTeam = blueTeamName
display.initialize(state.data)
for action in actions:
# Execute the action
state = state.generateSuccessor( *action )
# Change the display
display.update( state.data )
# Allow for game specific conditions (winning, losing, etc.)
rules.process(state, game)
display.finish()
def runGames( layouts, agents, display, length, numGames, record, numTraining, redTeamName, blueTeamName, muteAgents=False, catchExceptions=False ):
rules = CaptureRules()
games = []
if numTraining > 0:
print 'Playing %d training games' % numTraining
for i in range( numGames ):
beQuiet = i < numTraining
layout = layouts[i]
if beQuiet:
# Suppress output and graphics
import textDisplay
gameDisplay = textDisplay.NullGraphics()
rules.quiet = True
else:
gameDisplay = display
rules.quiet = False
g = rules.newGame( layout, agents, gameDisplay, length, muteAgents, catchExceptions )
g.run()
if not beQuiet: games.append(g)
g.record = None
if record:
import time, cPickle, game
#fname = ('recorded-game-%d' % (i + 1)) + '-'.join([str(t) for t in time.localtime()[1:6]])
#f = file(fname, 'w')
components = {'layout': layout, 'agents': [game.Agent() for a in agents], 'actions': g.moveHistory, 'length': length, 'redTeamName': redTeamName, 'blueTeamName':blueTeamName }
#f.close()
print "recorded"
g.record = cPickle.dumps(components)
with open('replay-%d'%i,'wb') as f:
f.write(g.record)
if numGames > 1:
scores = [game.state.data.score for game in games]
redWinRate = [s > 0 for s in scores].count(True)/ float(len(scores))
blueWinRate = [s < 0 for s in scores].count(True)/ float(len(scores))