def greedy_action(self, free_qblock_id_lists, collapsed_qttts, mark):
assert len(collapsed_qttts) > 0
states = {}
for i in range(len(collapsed_qttts)):
if free_qblock_id_lists[i] is None:
nstate = after_action_state(collapsed_qttts[i], None, mark)
states[(i, -1, -1)] = GameTree.get_state_val(nstate)
continue
n = len(free_qblock_id_lists[i])
for j in range(n - 1):
for k in range(j + 1, n):
loc1 = free_qblock_id_lists[i][j]
loc2 = free_qblock_id_lists[i][k]
nstate = after_action_state(collapsed_qttts[i],
(loc1, loc2), mark)
states[(i, loc1, loc2)] = GameTree.get_state_val(nstate)
if mark % 2 == 1:
indices = GameTree.best_states(states, min)
else:
indices = GameTree.best_states(states, max)
i, j, k = random.choice(indices)
action = (collapsed_qttts[i], (j, k))
return action
def _learn(self, max_episode, save_as_file='TD_policy.dat'):
env = Env()
agents = [
TD_agent(self.epsilon, self.alpha, self.decay_rate),
TD_agent(self.epsilon, self.alpha, self.decay_rate)
]
for _ in tqdm(range(max_episode)):
# reset to the initial state, env keep a counter for current round
# odd round->x, even round->o, because for each piece, it has a submark on it!
env.reset()
for agent in agents:
agent.decay_epsilon()
while True:
curr_qttt, mark = env.get_state()
agent = ProgramDriver.get_agent_by_mark(agents, mark)
free_qblock_id_lists, collapsed_qttts = env.get_valid_moves()
collapsed_qttt, agent_move = agent.act(free_qblock_id_lists,
collapsed_qttts, mark)
next_qttt, next_round, reward, done = env.step(
collapsed_qttt, agent_move, mark)
agent.bellman_backup(curr_qttt, next_qttt, reward, mark)
if done:
GameTree.set_state_value(next_qttt.get_state(), reward)
break
ProgramDriver.save_model(save_as_file, max_episode, self.epsilon,
self.alpha, self.decay_rate)
def __init__(self, initialState):
# initialize and construct GameTree for AI
self.gt = GameTree(initialState)
self.gt.expand() # only happens once during initialization
# keep track of current state
self.currState = initialState
def load_model(filename):
with open(filename, 'rb') as f:
# read model info
info = json.loads(f.readline().decode('ascii'))
for line in f:
elms = line.decode('ascii').split('\t')
state = eval(elms[0])
val = eval(elms[1])
vcnt = eval(elms[2])
GameTree.load_state(state, val, vcnt)
return info
def bellman_backup(self, qttt, next_qttt, reward, mark):
"""
Bellman backup for TD learning
:param Qttt state: current state of qttt
:param Qttt next_state: next state after action is take
:param int reward: immediate reward for this round
:return: None
"""
state_value = GameTree.get_state_val(qttt.get_state())
next_state_value = GameTree.get_state_val(next_qttt.get_state())
updated_state_value = state_value + self.alpha * (
reward + gamma * next_state_value - state_value)
GameTree.set_state_value(qttt.get_state(), updated_state_value)
def dfs(self):
#print "In dfs() of Play Class"
#root node of type GameTree
root=GameTree(self.startBoard)
#
possible_boards=root.getGameBoard().possibleBoards();
#print "Possible Board Values in Play Class::",possible_boards;
for nextBoard in possible_boards:
nextNode=GameTree(nextBoard);
#print "NextBoard Value in dfs() of Play Class::",nextBoard;
if(self.play(nextBoard,nextNode)):
root.addChild(nextNode);
def astar(self,heuristic_val):
#print "In dfs() of Play Class"
#root node of type GameTree
root=GameTree(self.startBoard)
#Expand Boards Based On The The Heuristics
possible_boards=root.getGameBoard().possibleBoards();
#print "Possible Board Values in Play Class::",possible_boards;
for nextBoard in possible_boards:
nextNode=GameTree(nextBoard);
#print "NextBoard Value in dfs() of Play Class::",nextBoard;
if(self.play(nextBoard,nextNode)):
root.addChild(nextNode);
class Game:
def __init__(self, initialState):
# initialize and construct GameTree for AI
self.gt = GameTree(initialState)
self.gt.expand() # only happens once during initialization
# keep track of current state
self.currState = initialState
def playOutGame(self, strat1, strat2): # returns path to get to end
# play to end
path = []
turnIter = 100
i = 1
while not self.currState.isTerminal():
if i % turnIter == 0:
print('# game at {} turns'.format(i))
path.append(self.currState)
if self.currState.turn == 1:
self.currState = strat1.calcNextMove(self)
else:
self.currState = strat2.calcNextMove(self)
i += 1
path.append(self.currState)
return path
def playPlayerGame(self, strat):
while not self.currState.isTerminal():
print("GameTree length: {}".format(len(self.gt.getAllNodes())))
print(self.currState, '\n')
# player's turn
if self.currState.turn == 1:
playerMove = input("You're up!\n")
if playerMove[0] == 's': # split move
self.currState = self.currState.splitMove(
int(playerMove[6]), int(playerMove[8]))
else:
self.currState = self.currState.strikeMove(
int(playerMove[0]), int(playerMove[2]))
# computer's turn
else:
print("My turn!")
self.currState = strat.calcNextMove(self)
print(self.currState)
print("Player {} wins!".format(self.currState.nextTurn()))
def AI_Move(self):
start = time.time()
self.winTime = 0
curBoard = copy.deepcopy(self.gb.board)
curList = self.gb.card_list.copy()
# print('preCard is: '+self.preCard)
root = GameTree(curBoard, '0000', curList, self.preCard)
curStep = 40 - (self.count_dict['color'] + self.count_dict['dot'] +
self.step_dict['color'] + self.step_dict['dot'])
minimax = MiniMax(curBoard, curList, curStep, self.current_turn,
self.isPuring, self.var_heuristic.get())
minimax.generateTree(1, root, curStep)
#sTime=time.time()
command = minimax.miniMaxi(root).id
#eTime=time.time()
#print('minimax use time: '+str(eTime-sTime)+' (s)')
if self.trace.get() == 1:
minimax.writeFile()
self.var_command.set(command)
self.isAI = True
self.button_move()
self.isAI = False
if self.winTime != 0:
end = self.winTime
else:
end = time.time()
self.lable_time['text'] = 'AI using time: ' + "{:.2f}".format(end -
start)
self.winTime = 0
def play_with_human(self, save_as_file='TD_human_policy.dat'):
ProgramDriver.load_model(save_as_file)
env = Env()
agents = [
TD_agent(self.epsilon, self.alpha, self.decay_rate),
HumanAgent(1),
]
while True:
env.reset()
td_agent = agents[0]
td_agent.decay_epsilon()
env.render()
while True:
curr_qttt, mark = env.get_state()
agent = ProgramDriver.get_agent_by_mark(agents, mark)
free_qblock_id_lists, collapsed_qttts = env.get_valid_moves()
collapsed_qttt, agent_move = agent.act(free_qblock_id_lists,
collapsed_qttts, mark)
if collapsed_qttt is None:
ProgramDriver.save_model(save_as_file, 0, self.epsilon,
self.alpha, self.decay_rate)
print("Model saved.")
sys.exit()
next_qttt, next_round, reward, done = env.step(
collapsed_qttt, agent_move, mark)
print('')
env.render()
td_agent.bellman_backup(curr_qttt, next_qttt, reward, mark)
if done:
GameTree.set_state_value(next_qttt.get_state(), reward)
next_qttt.show_result()
break
def __init__(self, manticore=None):
# user_account = m.create_account(balance=1000)
# user_sol_account = m.solidity_create_contract(contract_src, owner=user_account)
self.m = manticore
self.contract_account = self.m.create_account(balance=1000)
self.malicious_account = self.m.create_account(balance=1000)
self.contract_sol_account = self.m.solidity_create_contract(contract_src, owner=self.contract_account)
self.contract_sol_account._EVMContract__init_hashes()
self.root = GameTree(self.m,self.contract_sol_account)
self.symbolic_vars = {}
self.z3_var_counter = 0
self.z3_func = {}
self.lp = LtlParser()
def iddfs(self):
root = GameTree(self.startBoard)
depth_val = 0
ret_val = False
#every time initial configuration and hence infinite loop
#possible_boards=self.startBoard.possibleBoards();
#false value so run again
#true value so stop
while (not (ret_val)):
#print "In while loop with depth::",depth_val
ret_val = self.depth_ls(root, depth_val, self.startBoard)
#Increase depth after each iteration
depth_val = depth_val + 1
def save_model(save_file, max_episode, epsilon, alpha, decay_rate):
with open(save_file, 'wt') as f:
# write model info
info = dict(type="td",
max_episode=max_episode,
epsilon=epsilon,
alpha=alpha,
decay_rate=decay_rate)
# write state values
f.write('{}\n'.format(json.dumps(info)))
for state, value in GameTree.state_val.items():
# if value != 0:
vcnt = GameTree.get_state_cnt(state)
f.write('{}\t{:0.3f}\t{}\n'.format(state, value, vcnt))
def play(self, gb, parent):
node_counter = 0
if (gb.finalBoard()):
found = True
else:
found = False
nextBoards = gb.possibleBoards()
#print "NextBoard Values in Play Class::",nextBoards;
for nextBoard in nextBoards:
nextNode = GameTree(nextBoard)
if (self.play(nextBoard, nextNode)):
node_counter = node_counter + 1
parent.addChild(nextNode)
print "Number Of Nodes Expanded::", node_counter
return found
def dfs(self):
#print "In dfs() of Play Class"
#root node of type GameTree
root = GameTree(self.startBoard)
#
possible_boards = root.getGameBoard().possibleBoards()
#print "Possible Board Values in Play Class::",possible_boards;
for nextBoard in possible_boards:
nextNode = GameTree(nextBoard)
#print "NextBoard Value in dfs() of Play Class::",nextBoard;
if (self.play(nextBoard, nextNode)):
root.addChild(nextNode)
def astar(self, heuristic_val):
#print "In dfs() of Play Class"
#root node of type GameTree
root = GameTree(self.startBoard)
#Expand Boards Based On The The Heuristics
possible_boards = root.getGameBoard().possibleBoards()
#print "Possible Board Values in Play Class::",possible_boards;
for nextBoard in possible_boards:
nextNode = GameTree(nextBoard)
#print "NextBoard Value in dfs() of Play Class::",nextBoard;
if (self.play(nextBoard, nextNode)):
root.addChild(nextNode)
def depth_ls(self, parent, depth_val, gb):
if (depth_val >= 0):
#print "depth_val>=0::",depth_val>=0
nextBoards = gb.possibleBoards()
#print "nextBoard::",nextBoards
for nextBoard in nextBoards:
nextNode = GameTree(nextBoard)
if (self.depth_ls(nextNode, depth_val - 1, nextBoard)):
parent.addChild(nextNode)
#early detection for final board
if (nextBoard.finalBoard()):
return True
else:
#print "depth_val>=0",depth_val>=0
#invalid depth lookup so return
#print "Accessing nodes at invalid depth so returing::"
return False
from MetaGame import MetaGame
from GameTree import GameTree
import sys
try:
root = int(sys.argv[1])
tree = GameTree(MetaGame(root), 2)
print(tree.root().best_move(1000).get_meta_int())
except ValueError:
mode = sys.argv[1]
if mode == 'p':
root = int(sys.argv[2])
print(MetaGame(root))
elif mode == 'a':
depth = int(sys.argv[2])
root = int(sys.argv[3])
print(GameTree(MetaGame(root), depth + 1))
from .TableManager import Table
from .GameTree import GameTree, GameState
from .GameTable import GameTable
from .PrePostSim import PreSim, PostSim
from .PickPocket.MoveGenerator.Evaluate import EvalNode
from .PickPocket.utils.StopWatch import StopWatch
import Ipc.commands as commands
import json
import time
LookAheadDepth = 3
pipeline = PipeLine()
pipeline.open()
pipeline.WaitForArrival()
gametree = GameTree()
gametable = GameTable()
PreSimTask = PreSim(gametable=gametable)
VectorMath_StopWatch = StopWatch()
def RecvGreetings():
for msg in pipeline.recvr.RecvAll():
print(msg)
def CreatePreSimTask(move, table):
def presim_task():
gametable.ReserveTable(move)
cmd = commands.EncodeSGState(table, move)
pipeline.sender.Send(cmd)
