def init_mdp_old(self):
#get all network nodes
node_count = len(traci.junction.getIDList())
mdp = []
for node in range(0, node_count):
no = (self.network.getNode(str(node+1)))
list_edges = Node.getOutgoing(no)
#for each node find all outgoing edges
states = []
for l in list_edges:
#add the actions (edges) to the states (nodes): edge-id, q-value, reward (travel time on link)
states.append([str(Edge.getID(l)), -50*random.random(), -50*random.random()])
mdp.append(states)
return mdp
def init_mdp(self):
#get all network nodes
junction_list = traci.junction.getIDList()
usefull = [i for i in junction_list if not ':' in i]
mdp = dict([(x, []) for x in usefull])
for node in usefull:
no = (self.network.getNode(node))
list_edges = Node.getOutgoing(no)
states = dict([(x.getID(), []) for x in list_edges if not ':' in x.getID()])
#for each node find all outgoing edgesTrue
for e in list_edges:
#add the actions (edges) to the states (nodes): edge-id, q-value, reward (travel time on link)
states[e.getID()] = ([10*random.random(), 10*random.random()])
mdp[node] = states
return mdp
def process_vehicle(self, driver):
id_current_edge = traci.lane.getEdgeID(traci.vehicle.getLaneID(self.drivers[int(driver)].id))
#Driver needs a new route
if( self.isEdge(id_current_edge) and
(self.drivers[int(driver)].current_link == id_current_edge) and
(self.drivers[int(driver)].current_link!= self.drivers[int(driver)].destination) and
(self.drivers[int(driver)].isUpdate)):
next_node = self.get_destination_node(id_current_edge)
id_next_node = Node.getID(next_node)
action_key = ''
#next action,
if(random.random()<self.qlearning.epislon):
#returns one action randomly
action_key = self.drivers[int(driver)].mdp[id_next_node].keys()[random.randint(0, len(self.drivers[int(driver)].mdp[id_next_node].keys())-1)]
#exploitation
else:
#returns the action that minimize the travel_time the reward
action_key = self.return_best_action(driver, id_next_node)
#update q-table
#current q_value
current_q_value = self.drivers[int(driver)].mdp[id_next_node][action_key][0]
#action reward
reward = -1*self.drivers[int(driver)].mdp[id_next_node][action_key][1]
#node maximizes the reward on action
future_node = self.get_destination_node(action_key)
#id node maximizes the reward on action
id_new_future_edge = Node.getID(future_node)
#q_value of the best action
best_action = self.drivers[int(driver)].mdp[id_new_future_edge][self.return_best_action(driver, id_new_future_edge)][0]
#new q_value
q_value = ((1-self.qlearning.alpha)*current_q_value
+ self.qlearning.alpha*(abs(reward)+self.qlearning.gamma*best_action))
#updates q_value
self.drivers[int(driver)].mdp[id_next_node][action_key][0] = q_value
#if it's ok, update the route just on dijkstra
new_route = self.return_route(self.drivers[int(driver)].current_link, action_key)
#insert the new route in the vehicle
traci.vehicle.setRoute(self.drivers[int(driver)].id, new_route)
#update the driver steps
self.drivers[int(driver)].steps += 1
self.drivers[int(driver)].isUpdate = False
#update the travel time on the last action
elif(self.isEdge(id_current_edge) and (self.drivers[int(driver)].current_link!=id_current_edge)):
origin_node = self.get_origin_node(self.drivers[int(driver)].current_link).getID()
for action in self.drivers[int(driver)].mdp[origin_node]:
#set the total travel time on link
if str(action) == str(self.drivers[int(driver)].current_link):
link_tt = self.drivers[int(driver)].get_travel_time_on_link(self.get_time())
if link_tt < 0:
print 'errado'
self.drivers[int(driver)].mdp[origin_node][action][1] = link_tt
break
#update the drivers properties
self.drivers[int(driver)].current_link = id_current_edge
self.drivers[int(driver)].link_tt = self.get_time()
self.drivers[int(driver)].isUpdate = True
#The car is arriving on the destination
elif(self.isEdge(id_current_edge) and
(self.drivers[int(driver)].current_link==id_current_edge) and
(self.drivers[int(driver)].current_link == self.drivers[int(driver)].destination)):
last_node = self.get_origin_node(self.drivers[int(driver)].current_link)
next_node = self.get_destination_node(self.drivers[int(driver)].current_link).getID()
for action in self.drivers[int(driver)].mdp[last_node.getID()].keys():
if(action==str(self.drivers[int(driver)].current_link)):
'''in the future we need change this for the total travel time*-1'''
#total_tt = - self.drivers[int(driver)].get_total_travel_time(self.get_time())
self.drivers[int(driver)].mdp[last_node.getID()][action][0] = -self.drivers[int(driver)].get_total_travel_time(self.get_time())
break
#driver rechead his goal
#print '\t\tpassos ', self.drivers[int(driver)].steps
self.drivers[int(driver)].isArrived = True
self.running_drivers.remove(self.drivers[int(driver)].id)
