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MrRandTree.py
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MrRandTree.py
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#!/usr/bin/env python3
# -*- coding: UTF-8 -*-
from Util import log
from Util import ORDER_DICT2,SCORE_DICT
from MrRandom import MrRandom
from MrGreed import MrGreed
from ScenarioGenerator.ScenarioGen import ScenarioGen
from MCTS.mcts import mcts
import copy,itertools,numpy,time
print_level=0
class GameState():
def __init__(self,cards_lists,fmt_scores,cards_on_table,play_for):
self.cards_lists=cards_lists
self.cards_on_table=cards_on_table
self.fmt_scores=fmt_scores
self.play_for=play_for
#decide cards_dicts, suit and pnext
self.cards_dicts=[MrGreed.gen_cards_dict(i) for i in self.cards_lists]
if len(self.cards_on_table)==1:
self.suit="A"
else:
self.suit=self.cards_on_table[1][0]
self.pnext=(self.cards_on_table[0]+len(self.cards_on_table)-1)%4
self.remain_card_num=sum([len(i) for i in self.cards_lists])
def getCurrentPlayer(self):
if (self.pnext-self.play_for)%2==0:
return 1
else:
return -1
def getPossibleActions(self):
return MrGreed.gen_legal_choice(self.suit,self.cards_dicts[self.pnext],self.cards_lists[self.pnext])
def takeAction(self,action):
#log(action)
neo_state=copy.deepcopy(self)
neo_state.cards_lists[neo_state.pnext].remove(action)
neo_state.cards_dicts[neo_state.pnext][action[0]].remove(action)
neo_state.remain_card_num-=1
neo_state.cards_on_table.append(action)
#log(neo_state.cards_on_table)
#input()
assert len(neo_state.cards_on_table)<=5
if len(neo_state.cards_on_table)<5:
neo_state.pnext=(neo_state.pnext+1)%4
if len(neo_state.cards_on_table)==2:
neo_state.suit=neo_state.cards_on_table[1][0]
else:
#decide pnext
score_temp=-1024
for i in range(4):
if neo_state.cards_on_table[i+1][0]==neo_state.cards_on_table[1][0] and ORDER_DICT2[neo_state.cards_on_table[i+1][1]]>score_temp:
winner=i #in relative order
score_temp=ORDER_DICT2[neo_state.cards_on_table[i+1][1]]
neo_state.pnext=(neo_state.cards_on_table[0]+winner)%4
#clear scores
for c in neo_state.cards_on_table[1:]:
if c not in SCORE_DICT:
continue
neo_state.fmt_scores[neo_state.pnext][0]+=SCORE_DICT[c]
if c=='C10':
neo_state.fmt_scores[neo_state.pnext][2]=True
else:
neo_state.fmt_scores[neo_state.pnext][3]=True
if c[0]=='H':
neo_state.fmt_scores[neo_state.pnext][1]+=1
#clean table
neo_state.cards_on_table=[neo_state.pnext,]
neo_state.suit='A'
#log(neo_state.cards_on_table)
#log(neo_state.fmt_scores)
return neo_state
def isTerminal(self):
if self.remain_card_num==0:
return True
else:
return False
def getReward(self):
scores=[MrRandTree.clear_fmt_score(self.fmt_scores[(self.play_for+i)%4]) for i in range(4)]
"""scores_temp=copy.copy(scores)
c10=(i for i in range(4) if self.fmt_scores[i][2]).__next__()
if self.fmt_scores[c10][3]:
scores_temp[c10]/=2
else:
scores_temp[c10]=0
try:
assert sum(scores_temp)==-200
except:
log("%s %s"%(self.fmt_scores,scores))"""
#!TODO remember to change it later!
#return scores[0]-(scores[1]+scores[2]+scores[3])/3
return scores[0]+scores[2]-scores[1]-scores[3]
class MrRandTree(MrRandom):
N_SAMPLE=5
def clear_fmt_score(fmt_score):
"""
[0,0,False,False] stands for [score, #hearts, C10 flag, has score flag]
"""
s=fmt_score[0]
if fmt_score[1]==13:
s+=400
if fmt_score[2]:
if fmt_score[3]:
s*=2
else:
assert fmt_score[0]==0
assert fmt_score[1]==0
s=50
return s
def pick_a_card(self):
#确认桌上牌的数量和自己坐的位置相符
assert (self.cards_on_table[0]+len(self.cards_on_table)-1)%4==self.place
#utility datas
suit=self.decide_suit() #inherited from MrRandom
cards_dict=MrGreed.gen_cards_dict(self.cards_list)
#如果别无选择
if cards_dict.get(suit)!=None and len(cards_dict[suit])==1:
choice=cards_dict[suit][0]
if print_level>=1:
log("I have no choice but %s"%(choice))
return choice
if print_level>=1:
log("my turn: %s, %s"%(self.cards_on_table,self.cards_list))
fmt_scores=MrGreed.gen_fmt_scores(self.scores) #in absolute order， because self.scores is in absolute order
#log("fmt scores: %s"%(fmt_scores))
d_legal={c:0 for c in MrGreed.gen_legal_choice(suit,cards_dict,self.cards_list)} #dict of legal choice
sce_gen=ScenarioGen(self.place,self.history,self.cards_on_table,self.cards_list,number=MrRandTree.N_SAMPLE,METHOD1_PREFERENCE=100)
for cards_list_list in sce_gen:
cards_lists=[None,None,None,None]
cards_lists[self.place]=copy.copy(self.cards_list)
for i in range(3):
cards_lists[(self.place+i+1)%4]=cards_list_list[i]
if print_level>=1:
log("get scenario: %s"%(cards_lists))
cards_on_table_copy=copy.copy(self.cards_on_table)
gamestate=GameState(cards_lists,fmt_scores,cards_on_table_copy,self.place)
searcher=mcts(iterationLimit=200,explorationConstant=100)
searcher.search(initialState=gamestate)
for action,node in searcher.root.children.items():
if print_level>=1:
log("%s: %s"%(action,node))
d_legal[action]+=node.totalReward/node.numVisits
if print_level>=1:
log("d_legal: %s"%(d_legal))
input("press any key to continue...")
best_choice=MrGreed.pick_best_from_dlegal(d_legal)
return best_choice
@staticmethod
def family_name():
return 'MrRandTree'
def benchmark():
from MrRandom import MrRandom,Human
from MrIf import MrIf
from OfflineInterface import OfflineInterface
g=[MrGreed(room=0,place=i,name='greed%d'%(i)) for i in range(4)]
f=[MrIf(room=0,place=i,name="if%d"%(i)) for i in range(4)]
r=[MrRandom(room=0,place=i,name="random%d"%(i)) for i in range(4)]
rt=[MrRandTree(room=0,place=i,name='randtree%d'%(i)) for i in range(4)]
offlineinterface=OfflineInterface([f[0],g[1],f[2],g[3]],print_flag=False)
N1=1024;N2=2;stats=[]
log("%s vs. %s for %dx%d"%(offlineinterface.players[0].family_name(),offlineinterface.players[1].family_name(),N1,N2))
tik=time.time()
for k,l in itertools.product(range(N1),range(N2)):
if l==0:
cards=offlineinterface.shuffle()
else:
cards=cards[39:52]+cards[0:39]
offlineinterface.shuffle(cards=cards)
for i,j in itertools.product(range(13),range(4)):
offlineinterface.step()
"""if i==7 and j==2:
global print_level
print_level=1
offlineinterface.print_flag=True
log("start outputs")"""
stats.append(offlineinterface.clear())
offlineinterface.prepare_new()
if l==N2-1:
print("%4d"%(sum([j[0]+j[2]-j[1]-j[3] for j in stats[-N2:]])/N2),end=" ",flush=True)
#print("%s"%(stats[-1]),end=" ",flush=True)
tok=time.time()
log("time consume: %ds"%(tok-tik))
for i in range(4):
s_temp=[j[i] for j in stats]
log("%dth player: %.2f %.2f"%(i,numpy.mean(s_temp),numpy.sqrt(numpy.var(s_temp)/(len(s_temp)-1)),),l=2)
s_temp=[j[0]+j[2]-j[1]-j[3] for j in stats]
log("%.2f %.2f"%(numpy.mean(s_temp),numpy.sqrt(numpy.var(s_temp)/(len(s_temp)-1))))
if __name__=="__main__":
benchmark()