def main():
# Parameters
nGames = 1000
depth = 5
epsilon = [0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6]
search = 200
temperature = [0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6]
# Initialize
infoFile = open('MCTS_sweepET_info.txt', 'w')
infoFile.write(
'Games = ' + str(nGames) + '\n'
+ 'Depth = ' + str(depth) + '\n'
+ 'Epsilon = ' + str(epsilon) + '\n'
+ 'Search = ' + str(search) + '\n'
+ 'Temperature = ' + str(temperature) + '\n')
infoFile.close()
print('Start MCTS epsilon and temperature sweep!')
print('[Total games: %d]' % (nGames * len(epsilon) * len(temperature)))
print(
'[Depth: %d, Search: %d]'
% (depth, search))
HCAI = []
for i in range(1):
HCAI.append(c.actions[1])
for i in range(35):
HCAI.append(c.actions[0])
bResultsFile = open('MCTS_sweepET_b.txt', 'w')
wResultsFile = open('MCTS_sweepET_w.txt', 'w')
# Run the parameter sweep
for iEpsilon in range(len(epsilon)):
for iTemperature in range(len(temperature)):
print('Completed %1.1f' % (100 * (iEpsilon * len(epsilon) + iTemperature) / (len(epsilon) * len(temperature))) + '%')
bMCTSAI = MonteCarloTreeSearchAI.AI(c.team2, c.team3, depth, epsilon[iEpsilon], search, temperature[iTemperature])
bMCTSvHC = 0
wMCTSAI = MonteCarloTreeSearchAI.AI(c.team3, c.team2, depth, epsilon[iEpsilon], search, temperature[iTemperature])
wMCTSvHC = 0
for iGame in range(nGames):
game = Game.Game(c.team2, c.team3)
while game.running:
game.trainers[0].setNextAction(bMCTSAI.getAction(game.getState(c.amBlack)))
game.trainers[1].setNextAction(HCAI[game.round])
game.progress()
if game.win[0]:
bMCTSvHC += 1
game = Game.Game(c.team2, c.team3)
while game.running:
game.trainers[0].setNextAction(HCAI[game.round])
game.trainers[1].setNextAction(wMCTSAI.getAction(game.getState(c.amWhite)))
game.progress()
if game.win[1]:
wMCTSvHC += 1
bResultsFile.write(str(bMCTSvHC / nGames) + '\n')
wResultsFile.write(str(wMCTSvHC / nGames) + '\n')
print('Completed 100%')
bResultsFile.close()
wResultsFile.close()
def main():
# Parameters
nGames = 10
depth = 1
epsilon = 0.1
limit = 100
search = 250
temperature = 0.2
# Initialize
AI = [MonteCarloTreeSearchAI.AI(depth, epsilon, limit, search, temperature), RandomAI.AI()]
# Run speed test
runTimes = []
for iGame in range(nGames):
if iGame % int(nGames / 10) == 0:
print('Completed ' + str(int(100 * iGame / nGames)) + '%')
iTime = time.time()
game = Game.Game()
while game.running and game.round < limit:
states = [game.getState(True), game.getState(False)]
if game.forceSwitch[0] == game.forceSwitch[1]:
game.nextAction[0] = AI[0].getAction(states[0])
game.nextAction[1] = AI[1].getAction(states[1])
else:
for iT in range(2):
if game.forceSwitch[iT]:
game.nextAction[iT] = AI[iT].getAction(states[iT])
game.progress()
runTimes.append(time.time() - iTime)
tSum = sum(runTimes)
mean = tSum / nGames
distance = []
for i in range(nGames):
distance.append((runTimes[i] - mean) ** 2)
standardDeviation = math.sqrt(sum(distance) / nGames)
print(
'Completed 100%' + '\n'
+ 'Sum: ' + FormatTime.getString(tSum) + '\n'
+ 'Mean: ' + FormatTime.getString(mean) + '\n'
+ 'Standard deviation: ' + FormatTime.getString(standardDeviation))
def main():
# Parameters
nGames = 1000
depth = 5
epsilon = [0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6]
search = 200
temperature = [0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6]
# Initialize
HCAI = []
HC_string = ''
game = Game.Game()
for i in range(game.n - 1):
HCAI.append(0)
HC_string += '0'
for i in range(game.n):
HCAI.append(1)
HC_string += '1'
infoFile = open('MCTS_sweepET_info.txt', 'w')
infoFile.write(
'Games = ' + str(nGames) + '\n'
+ 'Depth = ' + str(depth) + '\n'
+ 'Epsilon = ' + str(epsilon) + '\n'
+ 'Search = ' + str(search) + '\n'
+ 'Temperature = ' + str(temperature) + '\n')
infoFile.close()
print('Start MCTS epsilon and temperature sweep!')
print('[Total games: %d]' % (nGames * len(epsilon) * len(temperature)))
print(
'[Depth: %d, Search: %d]'
% (depth, search))
resultsFile = open('MCTS_sweepET.txt', 'w')
# Run the parameter sweep
for iEpsilon in range(len(epsilon)):
for iTemperature in range(len(temperature)):
print(
'Completed %1.1f'
% (100 * (iEpsilon * len(epsilon) + iTemperature)
/ (len(epsilon) * len(temperature)))
+ '%')
strategies = {}
for iGame in range(nGames):
MCTSAI = MonteCarloTreeSearchAI.AI(
depth,
epsilon[iEpsilon],
search,
temperature[iTemperature])
game = Game.Game()
MCTS_string = ''
while game.running:
MCTS_action = MCTSAI.getAction(game.getState(True))
MCTS_string += str(MCTS_action)
game.progress([MCTS_action, HCAI[game.round]])
if MCTS_string in strategies:
strategies[MCTS_string] += 1
else:
strategies[MCTS_string] = 1
if HC_string in strategies:
resultsFile.write(str(strategies[HC_string] / nGames) + '\n')
else:
resultsFile.write(str(0.0) + '\n')
print('Completed 100%')
resultsFile.close()
def main():
# Parameters
nGames = 1000
depth = 25
epsilon = 0.2
search = 1000
temperature = 0.2
# Initialize
MCTSAI = MonteCarloTreeSearchAI.AI(depth, epsilon, search, temperature)
HCAI = []
HC_string = ''
game = Game.Game()
for i in range(game.n - 1):
HCAI.append(0)
HC_string += '0'
for i in range(game.n):
HCAI.append(1)
HC_string += '1'
print('Start MCTS benchmark!')
print('[Total games: %d]' % (nGames))
print(
'[Depth: %d, Epsilon: %1.1f, Search: %d, Temperature: %1.1f]'
% (depth, epsilon, search, temperature))
strategies = {}
MCTSvHC = 0
# Run the benchmark
for iGame in range(nGames):
if iGame % int(nGames / 10) == 0:
print('Completed ' + str(int(100 * iGame / nGames)) + '%')
game = Game.Game()
MCTS_string = ''
while game.running:
MCTS_action = MCTSAI.getAction(game.getState(True))
MCTS_string += str(MCTS_action)
game.progress([MCTS_action, HCAI[game.round]])
if game.win[0]:
MCTSvHC += 1
if MCTS_string in strategies:
strategies[MCTS_string] += 1
else:
strategies[MCTS_string] = 1
print('Completed 100%')
# Write the results
resultsFile = open('MCTS_benchmark_results.txt', 'w')
resultsFile.write('## Monte Carlo Tree Search\n')
resultsFile.write(
'Games: ' + str(nGames) + '\n'
+ 'Depth: ' + str(depth) + '\n'
+ 'Epsilon: ' + str(epsilon) + '\n'
+ 'Search: ' + str(search) + '\n'
+ 'Temperature: ' + str(temperature) + '\n\n')
resultsFile.write('# Optimal strategy usage: \n')
if HC_string in strategies:
resultsFile.write(str(100 * strategies[HC_string] / nGames) + '%\n\n')
else:
resultsFile.write('Did not use the optimal strategy at all')
resultsFile.write('# Win percentage: \n')
resultsFile.write(str(100 * MCTSvHC / nGames) + '%\n\n')
resultsFile.close()
def main():
# Parameters
nGames = 1000
depth = [5, 10, 15, 20, 25]
epsilon = 0.2
search = [200, 400, 600, 800, 1000]
temperature = 0.2
# Initialize
HCAI = []
HC_string = ''
game = Game.Game()
for i in range(game.n - 1):
HCAI.append(0)
HC_string += '0'
for i in range(game.n):
HCAI.append(1)
HC_string += '1'
infoFile = open('MCTS_sweepDS_info.txt', 'w')
infoFile.write(
'Games = ' + str(nGames) + '\n'
+ 'Depth = ' + str(depth) + '\n'
+ 'Epsilon = ' + str(epsilon) + '\n'
+ 'Search = ' + str(search) + '\n'
+ 'Temperature = ' + str(temperature) + '\n')
infoFile.close()
print('Start MCTS depth and search sweep!')
print('[Total games: %d]' % (nGames * len(depth) * len(search)))
print(
'[Epsilon: %1.1f, Temperature: %1.1f]'
% (epsilon, temperature))
resultsStrategyFile = open('MCTS_sweepDS_strategy.txt', 'w')
resultsTimeFile = open('MCTS_sweepDS_time.txt', 'w')
# Run the parameter sweep
for iDepth in range(len(depth)):
for iSearch in range(len(search)):
print(
'Completed %1.1f'
% (100 * (iDepth * len(depth) + iSearch)
/ (len(depth) * len(search)))
+ '%')
strategies = {}
runTime = time.time()
for iGame in range(nGames):
MCTSAI = MonteCarloTreeSearchAI.AI(
depth[iDepth],
epsilon,
search[iSearch],
temperature)
game = Game.Game()
MCTS_string = ''
while game.running:
MCTS_action = MCTSAI.getAction(game.getState(True))
MCTS_string += str(MCTS_action)
game.progress([MCTS_action, HCAI[game.round]])
if MCTS_string in strategies:
strategies[MCTS_string] += 1
else:
strategies[MCTS_string] = 1
if HC_string in strategies:
resultsStrategyFile.write(str(strategies[HC_string] / nGames) + '\n')
else:
resultsStrategyFile.write(str(0.0) + '\n')
resultsTimeFile.write(str((time.time() - runTime) / nGames) + '\n')
print('Completed 100%')
resultsStrategyFile.close()
resultsTimeFile.close()
def main():
# Parameters
nGames = 1000
depth = [1, 5, 10, 15, 20]
epsilon = 0.1
limit = 100
search = 250
temperature = 0.2
# Initialize
progressExist = os.path.isfile(
'MCTS_sweepD_results/MCTS_sweepD_progress.txt')
if progressExist:
array = Load.loadFloatArray('MCTS_sweepD_results/MCTS_sweepD_progress',
7)
startDepth = int(array[0])
startGame = int(array[1])
abort = array[2]
loss = array[3]
runTime = array[4]
tie = array[5]
win = array[6]
else:
startDepth = 0
if os.path.isfile('MCTS_sweepD_results/MCTS_sweepD_info.txt'):
exit(
'Info file exists but progress file doesn\'t: (Re)Move previous results before running!'
)
infoFile = open('MCTS_sweepD_results/MCTS_sweepD_info.txt', 'w')
infoFile.write('Games = ' + str(nGames) + '\n' + 'Depth = ' + str(depth) +
'\n' + 'Epsilon = ' + str(epsilon) + '\n' + 'Limit = ' +
str(limit) + '\n' + 'Search = ' + str(search) + '\n' +
'Temperature = ' + str(temperature) + '\n')
infoFile.close()
# Run the parameter sweep
for iDepth in range(startDepth, len(depth)):
print('Completed %1.1f' % (100 * iDepth / len(depth)) + '%')
if not progressExist:
startGame = 0
abort = 0
loss = 0
runTime = 0
tie = 0
win = 0
AI = [
MonteCarloTreeSearchAI.AI(depth[iDepth], epsilon, limit, search,
temperature),
RandomAI.AI()
]
for iGame in range(startGame, nGames):
iTime = time.time()
game = Game.Game()
while game.running and game.round < limit:
states = [game.getState(True), game.getState(False)]
if game.forceSwitch[0] == game.forceSwitch[1]:
game.nextAction[0] = AI[0].getAction(states[0])
game.nextAction[1] = AI[1].getAction(states[1])
else:
for iT in range(2):
if game.forceSwitch[iT]:
game.nextAction[iT] = AI[iT].getAction(states[iT])
game.progress()
runTime += time.time() - iTime
if game.win[0] and game.win[1]:
tie += 1
elif game.win[0]:
win += 1
elif game.win[1]:
loss += 1
else:
abort += 1
progressFile = open('MCTS_sweepD_results/MCTS_sweepD_progress.txt',
'w')
progressFile.write(
str(iDepth) + '\n' + str(iGame + 1) + '\n' + str(abort) +
'\n' + str(loss) + '\n' + str(runTime) + '\n' + str(tie) +
'\n' + str(win))
progressFile.close()
abortFile = open('MCTS_sweepD_results/MCTS_sweepD_abort.txt', 'a')
abortFile.write(str(abort / nGames) + '\n')
abortFile.close()
lossFile = open('MCTS_sweepD_results/MCTS_sweepD_loss.txt', 'a')
lossFile.write(str(loss / nGames) + '\n')
lossFile.close()
tieFile = open('MCTS_sweepD_results/MCTS_sweepD_tie.txt', 'a')
tieFile.write(str(tie / nGames) + '\n')
tieFile.close()
timeFile = open('MCTS_sweepD_results/MCTS_sweepD_time.txt', 'a')
timeFile.write(str(runTime / nGames) + '\n')
timeFile.close()
winFile = open('MCTS_sweepD_results/MCTS_sweepD_win.txt', 'a')
winFile.write(str(win / nGames) + '\n')
winFile.close()
progressFile = open('MCTS_sweepD_results/MCTS_sweepD_progress.txt',
'w')
progressFile.write(
str(iDepth + 1) + '\n' + str(0) + '\n' + str(0) + '\n' + str(0) +
'\n' + str(0) + '\n' + str(0) + '\n' + str(0))
progressFile.close()
progressExist = False
print('Completed 100%')
os.remove('MCTS_sweepD_results/MCTS_sweepD_progress.txt')
def main(id):
# Parameters
depth = 1
epsilon = 0.1
limit = 100
search = 10
temperature = 0.2
# Initialize
AI = [
RandomAI.AI(),
MonteCarloTreeSearchAI.AI(depth, epsilon, limit, search, temperature)
]
idExists = os.path.isfile('run_results/gamePrint_' + str(id) + '.txt')
while idExists:
id += 1
idExists = os.path.isfile('run_results/gamePrint_' + str(id) + '.txt')
gamePrintFile = open('run_results/gamePrint_' + str(id) + '.txt', 'w')
if os.path.isfile('run_results/results.txt'):
array = Load.loadFloatArray('run_results/results', 5)
initializedGames = array[0]
initializedGames += 1
completedGames = array[1]
losses = array[2]
ties = array[3]
wins = array[4]
else:
initializedGames = 1
completedGames = 0
losses = 0
ties = 0
wins = 0
resultsFile = open('run_results/results.txt', 'w')
resultsFile.write(
str(int(initializedGames)) + '\n' + str(int(completedGames)) + '\n' +
str(int(losses)) + '\n' + str(int(ties)) + '\n' + str(int(wins)) +
'\n')
resultsFile.close()
# Run the battle
game = Game.Game()
while game.running:
states = [game.getState(True), game.getState(False)]
if game.forceSwitch[0] == game.forceSwitch[1]:
thread0 = threading.Thread(target=setAction,
args=(game, 0, AI[0], states[0]))
thread0.daemon = True
thread0.start()
thread1 = threading.Thread(target=setAction,
args=(game, 1, AI[1], states[1]))
thread1.daemon = True
AI[1].extendSearch = True
thread1.start()
while thread1.isAlive():
if not thread0.isAlive():
AI[1].extendSearch = False
if AI[1].searchNotFinished:
print('\nPlease wait for the AI to make a decision!')
break
thread0.join()
thread1.join()
else:
for iT in range(2):
if game.forceSwitch[iT]:
game.nextAction[iT] = AI[iT].getAction(states[iT])
#stdout = sys.stdout
#sys.stdout = io.StringIO()
game.progress()
summary = game.getSummary()
print(summary[0:(len(summary) - 1)])
gamePrintFile.write(summary)
#gamePrint = sys.stdout.getvalue()
#gamePrintFile.write(gamePrint)
#sys.stdout = stdout
#print(gamePrint[0:(len(gamePrint) - 1)])
gamePrintFile.close()
# Save the results
completedGames += 1
if game.win[0] and game.win[1]:
ties += 1
print('Tie after ' + str(game.round) + ' rounds!')
elif game.win[0]:
wins += 1
print('Win after ' + str(game.round) + ' rounds!')
elif game.win[1]:
losses += 1
print('Loss after ' + str(game.round) + ' rounds!')
resultsFile = open('run_results/results.txt', 'w')
resultsFile.write(
str(int(initializedGames)) + '\n' + str(int(completedGames)) + '\n' +
str(int(losses)) + '\n' + str(int(ties)) + '\n' + str(int(wins)) +
'\n')
resultsFile.close()
def main():
# Parameters
nGames = 1000
depth = 10
search = 1000
bEpsilon = 0.2
bTemperature = 0.2
wEpsilon = 0.2
wTemperature = 0.2
# Initialize
print('Start MCTS benchmark!')
print('[Total games: %d]' % (2 * nGames))
print(
'[Depth: %d, Search: %d, Black epsilon: %1.1f, Black temperature: %1.1f, White epsilon: %1.1f, White temperature: %1.1f]'
% (depth, search, bEpsilon, bTemperature, wEpsilon, wTemperature))
HCAI = []
for i in range(1):
HCAI.append(c.actions[1])
for i in range(35):
HCAI.append(c.actions[0])
bMCTSAI = MonteCarloTreeSearchAI.AI(c.team2, c.team3, depth, bEpsilon, search, bTemperature)
bMCTSvHC = 0
wMCTSAI = MonteCarloTreeSearchAI.AI(c.team3, c.team2, depth, wEpsilon, search, wTemperature)
wMCTSvHC = 0
# Run the benchmark
for iGame in range(nGames):
if iGame % int(nGames / 10) == 0:
print('Completed ' + str(int(100 * iGame / nGames)) + '%')
game = Game.Game(c.team2, c.team3)
while game.running:
game.trainers[0].setNextAction(bMCTSAI.getAction(game.getState(c.amBlack)))
game.trainers[1].setNextAction(HCAI[game.round])
game.progress()
if game.win[0]:
bMCTSvHC += 1
game = Game.Game(c.team2, c.team3)
while game.running:
game.trainers[0].setNextAction(HCAI[game.round])
game.trainers[1].setNextAction(wMCTSAI.getAction(game.getState(c.amWhite)))
game.progress()
if game.win[1]:
wMCTSvHC += 1
print('Completed 100%')
# Write the results
resultsFile = open('MCTS_benchmark_results.txt', 'w')
resultsFile.write('## Monte Carlo Tree Search\n')
resultsFile.write(
'Games: ' + str(nGames) + '\n'
+ 'Depth: ' + str(depth) + '\n'
+ 'Search: ' + str(search) + '\n'
+ 'Black epsilon: ' + str(bEpsilon) + '\n'
+ 'Black temperature: ' + str(bTemperature) + '\n'
+ 'White epsilon: ' + str(wEpsilon) + '\n'
+ 'White temperature: ' + str(wTemperature) + '\n\n')
resultsFile.write('## Black \n\n')
resultsFile.write('# Win percentage: \n')
resultsFile.write(str(100 * bMCTSvHC / nGames) + '%\n\n')
resultsFile.write('## White \n\n')
resultsFile.write('# Win percentage: \n')
resultsFile.write(str(100 * wMCTSvHC / nGames) + '%\n\n')
resultsFile.close()