def problemA():
print("PROBLEM A...")
episodes = 10000
arr = np.zeros(episodes)
G = Gridworld()
G.gamma = 0.9
for e in range(episodes): # number of episodes loop
G.timeStep = 0
# print("episode %d" % (e+1))
while (not G.isEnd):
# print(G.currentState)
G.step(G.action)
arr[e] = G.reward
G.reset()
opt_disc_returns = np.amax(arr)
opt_episode = np.argmax(arr) + 1
mean = np.mean(arr)
variance = np.var(arr)
std_dev = np.std(arr)
min = np.amin(arr)
print("Highest observed discounted returns is %f achieved in"
" episode number %d" % (opt_disc_returns, opt_episode))
print("The mean of discounted returns is %f, variance is %f"
" and standard deviation is %f" % (mean, variance, std_dev))
print("Max is %f and min is %f" % (opt_disc_returns, min))
return arr
def problemE():
print("PROBLEM E...")
episodes = 10000
count = 0
G = Gridworld(startState=19)
G.gamma = 0.9
for e in range(episodes):
G.timeStep = 0
while ((G.timeStep < 11) and (not G.isEnd)):
G.step(G.action)
if G.state == 22:
count = count + 1
G.reset()
print("The empirical probability of S19 = 21 given S8 = 18 is %f" %
(count / episodes))
def problemE():
env = Gridworld(startState=18)
env.gamma = 0.9
episode = 0
hit = 0
total_try = 100000
while episode < total_try:
episode += 1
env.timeStep = 8
while env.timeStep < 19:
act = env.action
state, reward, isEnd = Gridworld.step(env, act)
if isEnd:
break
if env.currentState == 21:
hit += 1
print('P is {}'.format(hit / total_try))