def makeMove(self, state):
# if there is just 1 action (PASS), avoid the comptutation
actions = self.getAllActions(state)
res_actions = []
if len(actions) == 1:
return actions[0]
# do the sampling x times, then pick most common action
x = 10
for i in xrange(x):
time_start = time.time()
cardsLeft = cards.diff(cards.allCards(),
[state.playedCards, self.hand])
otherRemaining = list(state.numRemaining)
del otherRemaining[self.idx]
hands = cards.dealHands(cardsLeft, otherRemaining)
hands.insert(self.idx, dict(self.hand))
root = mctsNode(state.playedCards, self.idx, hands, self.idx, None,
0, state.topCard, state.lastPlayed, state.finished)
loop_count = 0
while time.time() < time_start + budget:
loop_count += 1
nextNode = self.selection(root)
result = self.simulation(nextNode)
self.backpropagation(nextNode, result)
sorted_children = sorted(
root.children, key=lambda child: child.score / child.visits)
res_actions.append(sorted_children[-1].lastMove)
numActions = Counter(res_actions).most_common()
return numActions[0][0]
def makeMove(self, state):
# if there is just 1 action (PASS), avoid the comptutation
actions = self.getAllActions(state)
res_actions = []
if len(actions) == 1:
return actions[0]
# do the sampling x times, then pick most common action
x = 10
for i in xrange(x):
time_start = time.time()
cardsLeft = cards.diff(cards.allCards(), [state.playedCards, self.hand])
otherRemaining = list(state.numRemaining)
del otherRemaining[self.idx]
hands = cards.dealHands(cardsLeft , otherRemaining)
hands.insert(self.idx, dict(self.hand))
root = mctsNode(state.playedCards, self.idx, hands, self.idx,
None, 0, state.topCard, state.lastPlayed, state.finished)
loop_count = 0
while time.time() < time_start + budget:
loop_count += 1
nextNode = self.selection(root)
result = self.simulation(nextNode)
self.backpropagation(nextNode, result)
sorted_children = sorted(root.children, key = lambda child: child.score/child.visits)
res_actions.append(sorted_children[-1].lastMove)
numActions = Counter(res_actions).most_common()
return numActions[0][0]
def simulate(args):
"""Function to simulate the other players' cards randomly and play out the
paranoid game tree based on those hands. Returns the best action.
"""
global nodesExpanded
nodesExpanded = 0
trial, state, index, hand = args
# subtract played cards and your own hand from cards remaining
cardsLeft = cards.diff(cards.allCards(), [state.playedCards, hand])
# get number of remaining cards for everyone else and deal hands
withoutMe = list(state.numRemaining)
del withoutMe[index]
hands = cards.dealHands(cardsLeft, withoutMe)
# put my hand back in
hands.insert(index, hand)
agents = map(lambda (i,h): ParanoidAgent(i, h),
zip(xrange(state.numPlayers), hands))
res = paranoid(state, 1, agents, -(sys.maxint -1), sys.maxint)
return res[0], nodesExpanded
def simulate(args):
"""Function to simulate the other players' cards randomly and play out the
paranoid game tree based on those hands. Returns the best action.
"""
global nodesExpanded
nodesExpanded = 0
trial, state, index, hand = args
# subtract played cards and your own hand from cards remaining
cardsLeft = cards.diff(cards.allCards(), [state.playedCards, hand])
# get number of remaining cards for everyone else and deal hands
withoutMe = list(state.numRemaining)
del withoutMe[index]
hands = cards.dealHands(cardsLeft, withoutMe)
# put my hand back in
hands.insert(index, hand)
agents = map(lambda (i, h): ParanoidAgent(i, h),
zip(xrange(state.numPlayers), hands))
res = paranoid(state, 1, agents, -(sys.maxint - 1), sys.maxint)
return res[0], nodesExpanded
def addChild(self, action):
# get successor state from state module
curr_state = state.State(self.playedCards, self.whosTurn, self.topCard,
self.lastPlayed, self.finished)
newState = curr_state.getChild(action)
if action == agent.PASS:
newHands = list(self.hands)
else:
player_hand = dict(self.hands[self.whosTurn])
new_hand = cards.diff(player_hand, {action[1]: action[0]})
newHands = list(self.hands)
newHands[self.whosTurn] = new_hand
score = 0.
# if agent got rid of cards, initialize score to finishing position
if self.idx in newState.finished:
score = (newState.finished.index(self.idx) + 1)**-1
newNode = mctsNode(newState.playedCards, newState.whosTurn, newHands,
self.idx, action, self.depth + 1, newState.topCard,
newState.lastPlayed, newState.finished, self, score)
self.children.append(newNode)
def addChild(self, action):
# get successor state from state module
curr_state = state.State(self.playedCards, self.whosTurn,
self.topCard, self.lastPlayed, self.finished)
newState = curr_state.getChild(action)
if action == agent.PASS:
newHands = list(self.hands)
else:
player_hand = dict(self.hands[self.whosTurn])
new_hand = cards.diff(player_hand, {action[1]: action[0]})
newHands = list(self.hands)
newHands[self.whosTurn] = new_hand
score = 0.
# if agent got rid of cards, initialize score to finishing position
if self.idx in newState.finished:
score = (newState.finished.index(self.idx) + 1) ** -1
newNode = mctsNode(newState.playedCards, newState.whosTurn, newHands,
self.idx, action, self.depth + 1, newState.topCard,
newState.lastPlayed, newState.finished, self, score)
self.children.append(newNode)
def simulate(args):
"""Function to simulate the other players' cards randomly and play out the
max^n tree based on those hands. Returns the best action.
:trialNum: Trial number (for debugging and unique identification).
:node: The current State object.
:idx: The index of the current player.
:hand: The current player's hand, which is known.
:returns: The action tuple corresponding to the best action to take.
"""
trialNum, node, idx, hand = args
# subtract played cards and your own hand from cards remaining
cardsLeft = cards.diff(cards.allCards(), [node.playedCards, hand])
# get number of remaining cards for everyone else and deal hands
withoutMe = list(node.numRemaining)
del withoutMe[idx]
hands = cards.dealHands(cardsLeft, withoutMe)
# put my hand back in
hands.insert(idx, hand)
agents = map(lambda (i,h): MaxNAgent(i, h),
zip(xrange(node.numPlayers), hands))
bestAct, bestVal = maxN(node, agents, 0, 2*node.numPlayers)
return bestAct