def iteration_de_la_valeur(g, p, gamma, epsilon):
global liste_actions
start = tm.time()
#2 etats : - pour t-1
# - pour t
val_etats = np.zeros((len(g), len(g[0])))
vt = np.zeros((len(g), len(g[0])))
q = np.zeros((len(g), len(g[0]), len(liste_actions)))
t = 0
#critere d'arret
while np.max(np.abs(vt - val_etats)) >= epsilon or t == 0:
t += 1
#etat precedant
vt = np.copy(val_etats)
for i in range(
len(val_etats)): #TODO : plus propre de mettre nbLignes non ?
for j in range(len(val_etats[0])): # pareil
for action in liste_actions:
r = -g[i][j][1]
q[i][j][action] = r + gamma * ot.sum_p_v(
g, val_etats, i, j, p, action)
val_etats[i][j] = max(q[i][j])
#politique optimal
d = np.zeros((len(g), len(g[0])))
for i in range(len(val_etats)):
for j in range(len(val_etats[0])):
d[i][j] = np.argmax(q[i][j])
end = tm.time()
time = end - start
return d, val_etats, t, time
def iteration_de_la_politique(g, p, gamma, epsilon):
global liste_actions
#2 etats : - pour t-1
# - pour t
start = tm.time()
val_etats = np.zeros((len(g), len(g[0])))
d = np.zeros((len(g), len(g[0])))
t = 0
vt = val_etats, val_etats
#critere d'arret
while np.max(np.abs(vt - val_etats)) >= epsilon or t == 0:
# while not(np.array_equal(d_pred,d)) or t == 0:
t += 1
#valeur de la politique courant
vt = np.copy(val_etats)
#evaluation de la politique courante
for i in range(len(val_etats)):
for j in range(len(val_etats[0])):
r = -g[i][j][1]
#arrivé au but
val_etats[i][j] = r + gamma * ot.sum_p_v(
g, val_etats, i, j, p, d[i][j])
#amelioration de la politique
for i in range(len(val_etats)):
for j in range(len(val_etats[0])):
arg = []
for action in liste_actions:
#arrivé au but
r = -g[i][j][1]
arg.append(r + gamma *
ot.sum_p_v(g, val_etats, i, j, p, action))
arg = np.array(arg)
d[i][j] = np.argmax(arg)
# print(g)
# print(ot.from_action_to_dir(d,g))
# #print(v)
# print(t)
end = tm.time()
time = end - start
return d, val_etats, t, time
def pl(g,p,gamma):
global liste_actions
start = tm.time()
# Create a new model
m = grb.Model("test")
val_etats = []
# Create variables
for i in range(len(g)):
for j in range(len(g[0])):
n = "v["+str(i)+"]["+str(j)+"]"
r = - g[i][j][1]
x = m.addVar(vtype=grb.GRB.CONTINUOUS, name=n)
val_etats.append(x)
val_etats = np.array(val_etats)
val_etats = np.reshape(val_etats,(len(g),len(g[0])))
m.setObjective(np.sum(val_etats), grb.GRB.MINIMIZE)
for i in range(len(g)):
for j in range(len(g[0])):
for action in liste_actions:
r = -g[i][j][1]
m.addConstr(val_etats[i][j] >= r + gamma * ot.sum_p_v(g, val_etats, i, j, p, action))
m.optimize()
val_etats = np.reshape(np.array([v.x for v in m.getVars()]),(len(g),len(g[0])))
d = np.zeros((len(g), len(g[0])))
for i in range(len(val_etats)):
for j in range(len(val_etats[0])):
val_par_action = np.zeros(len(liste_actions))
for action in liste_actions:
val_par_action[action] = r + gamma * ot.sum_p_v(g, val_etats, i, j, p, action)
d[i][j] = np.argmax(val_par_action)
end = tm.time()
time = end - start
#nombre d'iteration
t = m.getAttr('IterCount')
return d, val_etats, t, time
def pl(g, nbLignes, nbCol, liste_actions, rewards, p, gamma, epsilon):
# Create a new model
m = Model("test")
val_etats = []
# Create variables
for i in range(nbLignes):
for j in range(nbCol):
n = "v[" + str(i) + "][" + str(j) + "]"
r = rewards[g[i][j]]
if i == len(g) - 1 and j == len(g[0]) - 1:
r = rewards[-1]
x = m.addVar(vtype=GRB.CONTINUOUS, name=n)
val_etats.append(x)
# Set objective
# print(val_etats[0])
# print(m.getVars())
# for v in m.getVars():
# print('la')
# print('%s %g' % (v.varName, v.x))
# print(y)
val_etats = np.array(val_etats)
val_etats = np.reshape(val_etats, (nbLignes, nbCol))
m.setObjective(np.sum(val_etats), GRB.MINIMIZE)
print((val_etats.shape))
for i in range(nbLignes):
for j in range(nbCol):
for action in liste_actions:
r = outils.reward_action(g, i, j, action, rewards)
if r:
if i == len(g) - 1 and j == len(g[0]) - 1:
r = rewards[-1]
m.addConstr(
val_etats[i][j] >= r +
gamma * outils.sum_p_v(g, val_etats, i, j, p, action))
m.optimize()
# for v in m.getVars():
# print('%s %g' % (v.varName, v.x))
val_etats = np.reshape(np.array([v.x for v in m.getVars()]),
(nbLignes, nbCol))
# print(val_etats)
# print(val_etats.shape)
# for v in m.getConstrs():
# print('%s' % (v.getAttr("slack")))
# print('Obj: %g' % m.objVal)
# Affiche le pl dans un fichier
# m.write('debug.lp')
d = np.zeros((nbLignes, nbCol))
for i in range(len(val_etats)):
for j in range(len(val_etats[0])):
val_par_action = np.zeros(len(liste_actions))
for action in liste_actions:
val_par_action[action] = r + gamma * outils.sum_p_v(
g, val_etats, i, j, p, action)
d[i][j] = np.argmax(val_par_action)
return d, val_etats