def drawMonteCarlo():
iterations = [10, 100, 1000, 10000, 100000, 500000, 1000000]
for iteration in iterations:
print('Creating Monte Carlo Agent...')
monti = MonteCarlo(100)
print('Monte Carlo created')
print('Training Monte Carlo for', iteration, 'iterations.')
monti.train(iteration)
print('Training completed, plotting image')
figure = plt.figure('Monte' + str(iteration))
b = figure.add_subplot(111, projection='3d')
resultfig = plotMonte(b, monti)
figure.savefig('MonteCarlo' + str(iteration) + '.png')
plt.show()
def drawForLambdaZero():
montecarlo = MonteCarlo(100)
print('Training Monte Carlo')
montecarlo.train(500000)
print('Training of Monte Carlo Completed')
lambdaValue = 0
learningRate = []
learningRateIndex = []
sarsa = SARSA(100, lambdaValue)
print('Training SARSA and plotting graph')
for i in range(1000):
learningRateIndex.append(i)
sarsa.train(1)
squareMean = np.sum(np.square(sarsa.Q - montecarlo.Q)) / float(1000)
learningRate.append(squareMean)
fig = plt.figure("SARSAZERO")
surf = plt.plot(learningRateIndex, learningRate)
fig.savefig('lambdaZero.png')
plt.show()
def drawForAllLambdas():
montecarlo = MonteCarlo(100)
print('Training Monte Carlo')
montecarlo.train(500000)
print('Training of Monte Carlo Completed')
lambdas = [0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0]
squareMean = []
numberElements = montecarlo.Q.shape[0] * montecarlo.Q.shape[1] * 2
for lambdaValue in lambdas:
sarsa = SARSA(100, lambdaValue)
print('Training SARSA', lambdaValue)
sarsa.train(1000)
print('Training of SARSA Completed')
squareMeanCalc = np.sum(
np.square(sarsa.Q - montecarlo.Q)) / float(numberElements)
squareMean.append(squareMeanCalc)
fig = plt.figure("SARSA")
surf = plt.plot(lambdas[1:10], squareMean[1:10])
fig.savefig('lambdaALL.png')
plt.show()
