def main():
#read in results file
results = open("results.txt","r")
questions = []
for line in results:
line = line.replace("\n","")
line = line.replace("\r","")
questions.append(line.split(","))
windows = []
#do queries for each line in the file
for q in questions:
window = tk.Tk()
grid = Grid('gridConf.txt')
if(q[3] == "MDP"):
valueIteration = ValueIteration(grid)
grid = valueIteration.runValueIteration()
elif(q[3] == "RL"):
qValueLearning = QValueLearning(grid)
grid = qValueLearning.runQValueLearning()
gridPolicies = grid.get_policies_()
terminal_states = grid.terminal
boulder_states = grid.boulder
answer = ""
if(q[4] == "stateValue"):
answer = grid.gridStates[int(q[1])][int(q[0])].get_max()
elif(q[4] == "bestPolicy"):
answer = grid.gridStates[int(q[1])][int(q[0])].getPolicy(0.0)[1]
elif(q[4] == "bestQValue" and q[3] == "RL"):
answer = grid.gridStates[int(q[1])][int(q[0])].getPolicy(0.0)[0]
index = questions.index(q) + 1
answer = "Question " + str(index) + ": " + ",".join(q) + ": " + str(answer)
if(q[3] == "MDP"):
draw_board(window, gridPolicies, [row[:-1] for row in terminal_states], boulder_states,
max_reward(terminal_states), max_punishment(terminal_states), q[2], 'value-iteration', answer)
elif(q[3] == "RL"):
draw_board(window, gridPolicies, [row[:-1] for row in terminal_states], boulder_states,
max_reward(terminal_states), max_punishment(terminal_states), q[2], 'q-learning', answer)
windows.append(window)
#display all queries
for window in windows:
window.mainloop()
