def update_q(state, action, new_q):
state = str(state)
action = str(action)
actionBST = q_values.findval(state, False)
if not actionBST:
actionBST = BST.Node(str([]), 0)
q_values.insert(state, actionBST)
actionBST.update(action, new_q)
def getHeight(node):
if node is None:
return False
else:
marker = BST.Node()
node.key = 323233223
queue = Queue.Queue()
queue.enqueue(node)
queue.enqueue(marker)
count = -1
while not queue.isempty():
temp = queue.dequeue()
if temp == marker:
count += 1
if not queue.isempty():
queue.enqueue(marker)
else:
if temp.left is not None:
queue.enqueue(temp.left)
if temp.right is not None:
queue.enqueue(temp.right)
return count
def sendval():
global count
global r
if request.method == 'POST':
val = request.args.get('val')
text = request.args.get('text')
text = nltk.word_tokenize(text)
tagged_word = nltk.pos_tag(text)
nouns_list = []
for i in tagged_word:
if i[1][:2] == 'NN':
nouns_list.append(i[0])
if count == 0:
r = BST.Node(val, nouns_list)
else:
r = BST.insert(r, val, nouns_list)
count += 1
my_pickle = open("BST_root.pickle", "wb")
pickle.dump(r, my_pickle)
my_pickle.close()
return {val: nouns_list}
import pickle
import BST
from Environment import get_actions, environment, calc_length
# variables
vehicle_number_weight = 0.5
seed = 2021
# learning variables
epsilon = 0.7
discount_factor = 1
learning_rate = 0.7
iterations = 2000
RNG = np.random.RandomState(seed)
# q value for state action pairs (in route, new_route)
q_values = BST.Node(str([]), 0)
emptyNode = BST.Node(str([]), 0)
q_value_init = 0
def get_q(state, action):
state = str(state)
action = str(action)
return q_values.findval(state, emptyNode).findval(action, q_value_init)
def update_q(state, action, new_q):
state = str(state)
action = str(action)
actionBST = q_values.findval(state, False)
if not actionBST:
