# L.Braun 2018 # Main program to solve a gridworld maze problem # Uses qlearner.py, environ.py from qlearner import QLearner import pylab as plt my_learner = QLearner() my_learner.load_maze('/u/braun/tlab/QLearner/data/reward_4x4.npy', '/u/braun/tlab/QLearner/data/meta_4x4.txt') #print ("testing data load\n\n") #my_learner.display_Q() #my_learner.display_R() print("begin training...") reward = my_learner.train(0.7) my_learner.display_Q() my_learner.display_R() steps = my_learner.test(7) # 7 foods in 4x4 maze print("steps") print(steps) print("") plt.hist(reward, 50, normed=1, facecolor='g', alpha=0.75) plt.xlabel('Episodes required to reach 200')