if successor.isWin():
successor = None
elif successor.isLose():
# reward = -100
successor = None
output.append((successor, prob, reward))
return output
def discount(self):
return self.tetris.discount()
if __name__ == "__main__":
# weights = TetrisGame.readWeights('weightsThiery.tetris')
#weights = [0.10000000000000001, 0.5, -44.892192895842715, -88.868713917352991, -9.0845044328524125, 99.48473353935913, -0.10000000000000001, -0.10000000000000001, -46.3552758541178784, -44.894981251780582, -49.903753991739844, -48.776916252514777, -56.009249265806467, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
# weights = [random.random() for _ in xrange(22)]
weights = TetrisGame.readWeights('weights_ql400.tetris')
# evaluator = AdvancedEvaluator(weights)
evaluator = AdhocEvaluator(weights)
baseSeq = [0,1,2,3,4,5,6]*100
# random.seed(21)
random.shuffle(baseSeq);
# state: (board, new piece)
# action: (newGrid, reward)
def featureExtractor(state, action):
newState = state.generateSuccessor(0, action)
output = evaluator.featureExtractor(newState)
return output
model = TetrisGameMDP(TetrisMDP(), ExpectimaxTetrisAgent(0, 1, evaluator), FinitePieceGenerator(baseSeq))
print 'std', std
print '5%', percent5
print '95%', percent95
return mean, std, percent5, percent95
def printArr(arr):
print '=========='
for a in arr:
print a
if __name__ == '__main__':
# weightFile = 'weights_ql500.tetris'
results_lines = []
results_scores = []
for i in xrange(9,21):
weightFile = 'weights_ce{}.tetris'.format(i)
weights = game.readWeights(weightFile)
linesV, scoresV = simulateGame(weights, 10)
print '===lines==='
results_lines.append(analyze(linesV))
print '===scores==='
results_scores.append(analyze(scoresV))
results1 = zip(*results_lines)
results2 = zip(*results_scores)
print len(results1), len(results2)
for result in results1:
printArr(result)
for result in results2:
printArr(result)
