def main():
quit = -1
while quit < 0:
print "-" * 30 + " Project 1 " + "-" * 30
# print "Choose:"
print "\n\n For a: \n 1: Graph Figure 7.16 in [GT] page 364 \n Default values (from = 'BWI', to = ['SFO','LAX']) \n\n For b: \n 2: MC with (3,2) \n 3: MC with (4,3) \n\n To Quit: \n 4: quit\n\n"
choice = input('Enter your choise: ')
if choice == 4:
quit = 1
elif choice == 3:
mc.main(4, 3)
elif choice == 2:
mc.main(3, 2)
elif choice == 1:
gr.main()
plt.ylabel('Win Rate')
plt.title('Summary of ' + a1Name + ' and ' + a2Name + ' over episodes')
plt.legend(loc="upper left")
plt.show()
if __name__ == '__main__':
mcAverageMoves = []
qlAverageMoves = []
rAverageMoves = []
for i in range(50, 1001, 50):
aveMC = []
aveQL = []
aveR = []
for j in range(20):
b1 = MC.main(i)
mcMoves = totalMoves(b1)
aveMC.append(mcMoves)
b2 = QL.main(i)
qlMoves = totalMoves(b2)
aveQL.append(qlMoves)
rAgent = Agent(h, w)
randomAgent(rAgent)
rMoves = totalMoves(rAgent.enemyBoard)
aveR.append(rMoves)
#arrays of shape (20,20). 20 episodes and 20 games per episode
mcAverageMoves.append(aveMC)
qlAverageMoves.append(aveQL)
# Run this file to see that our code works
import QL
import MC
print("Running Monte-Carlo learning algorithm for 1000 episodes...")
print("Please exit Monte-Carlo graph to continue")
MC.main(1000)
print("Running Q-learning learning algorithm for 1000 episodes...")
QL.main(1000)
