def construct_RT(self):
''' n x 1'''
self.RT = np.zeros((self.n,1))
for i in range(self.n):
state_vec = self.sdic.get_state(i)
# if the current position is equal to destination, give a reward
if state.same_loc([state_vec[0], state_vec[1]], [state_vec[2], state_vec[3]]):
self.RT[i,0] = REWARD
# if the current position is equal to start, give a cost
else:
dest = [state_vec[2], state_vec[3]]
start = START[DESTINATION.index(dest)]
if state.same_loc([state_vec[0], state_vec[1]], [start[0], start[1]]):
self.RT[i,0] = COST
else:
self.RT[i,0] = 0.5
def construct_RT(self):
''' n x 1'''
self.RT = np.zeros((self.n,1))
for i in range(self.n):
state_vec = self.sdic.get_state(i)
# if the current position is equal to destination
if state.same_loc([state_vec[0], state_vec[1]], [state_vec[2], state_vec[3]]):
self.RT[i,0] = REWARD
def construct_x0(self):
''' n x 1, assume cars are distributed equally in start '''
self.x0 = np.zeros((self.n, 1))
for i in range(self.n):
state_vec = self.sdic.get_state(i)
start_pos = [state_vec[0], state_vec[1]]
if not(start_pos in START):
continue
else:
des_pos = [state_vec[2], state_vec[3]]
if state.same_loc(DESTINATION[START.index(start_pos)], des_pos):
self.x0[i, 0] = INIT_DENSITY_CORNER
def construct_G(self):
'''A x n x n'''
self.G = np.zeros((self.A, self.n, self.n))
for act in range(self.A):
if act == STAY:
self.G[act,:,:] = np.eye(self.n) # stay results in an identity matrix
else:
# probability from j to i
G_act = np.zeros((self.n, self.n))
for j in range(self.n):
state_j = self.sdic.get_state(j) # start from this state
loc_j = [state_j[0], state_j[1]]
result_loc = self.world.move_consq(loc_j, act)
for i in range(self.n):
state_i = self.sdic.get_state(i)
loc_i = [state_i[0], state_i[1]]
if state.same_loc(result_loc, loc_i) \
and state_i[2] == state_j[2] and state_i[3] == state_j[3] and state_i[4] == state_j[4]:
G_act[i][j] = 1
self.G[act,:,:] = cp.deepcopy(G_act)
