def value_iteration(self, epsilon=0.001, policy=None, k=100000, _Uvec=None, _stationary=True):
"""Solving a VVMdp by value iteration. The weight vector Lambda is known and used to compute scalar
value functions, so that is the standard VI algorithm [Fig. 17.4]. Stops when the improvement is
less than epsilon"""
n, na, d, Lambda = self.nstates, self.nactions, self.d, self.Lambda
gamma, R, expected_scalar_utility = self.gamma, self.rewards, self.expected_scalar_utility
Udot = np.zeros(n, dtype=ftype)
uvec = np.zeros(d, dtype=ftype)
Uvec = np.zeros((n, d), dtype=ftype)
# for test
lastp = np.zeros(n, dtype=np.int16)
newp = np.zeros(n, dtype=np.int16)
if _Uvec is not None:
Uvec[:] = _Uvec
Q = np.zeros(na, dtype=ftype)
for t in range(k): # bounds the number of iterations if the break condition is too weak
delta = 0.0
for s in range(n):
# Choose the action
if policy is not None:
if _stationary:
act = random.choice(policy[s])
else:
act = policy[s]
else:
Q[:] = [expected_scalar_utility(s, a, Udot) for a in range(na)]
act = np.argmax(Q)
newp[s] = act
# Compute the update
uvec[:] = R[s] + gamma * self.expected_vec_utility(s, act,
Uvec) # vectorial utility of the best action
udot = Lambda.dot(uvec) # its scalar utility
if policy is not None:
delta = max(delta, l1distance(uvec, Uvec[s]))
else:
delta = max(delta, abs(udot - Udot[s]))
Uvec[s] = uvec
Udot[s] = udot
if (newp - lastp).any():
#print t, ":", newp, Udot
lastp[:] = newp
if delta < epsilon * (1 - gamma) / gamma: # total expected improvement for adding delta
#print t, ":", newp, Udot
return Uvec
return Uvec
def value_iteration(self, epsilon=0.001,policy=None,k=100000,_Uvec=None, _stationary= True):
"Solving an MDP by value iteration. [Fig. 17.4]"
n , na, d , Lambda = self.nstates , self.nactions, self.d , self.Lambda
gamma , R , expected_scalar_utility = self.gamma , self.rewards , self.expected_scalar_utility
Udot = np.zeros( n , dtype=ftype)
_uvec= np.zeros( d , dtype=ftype)
#Rdot = np.array( [R[s].dot(Lambda) for s in range(n)] ,dtype=ftype)
Uvec = np.zeros( (n,d) , dtype=ftype)
if _Uvec != None:
Uvec[:] = _Uvec
Q = np.zeros( na , dtype=ftype )
for t in range(k):
delta = 0.0
for s in range(n):
# Choose the action
if policy != None:
if _stationary:
act = random.choice(policy[s])
else:
act = policy[s]
else:
Q[:] = [expected_scalar_utility(s, a, Udot) for a in range(na)]
act = np.argmax(Q)
# Compute the update
_uvec[:] = R[s] + gamma * self.expected_vec_utility(s,act,Uvec)
_udot = Lambda.dot(_uvec)
if policy != None:
delta = max(delta, l1distance(_uvec , Uvec[s]) )
else:
#print "old delta=",delta," , new delta=",max(delta, abs(_udot-Udot[s]) )
#print "_udot=",_udot," , Udot[s]=",Udot[s]
#print "_uvec=",_uvec," , Uvec[s]=",Uvec[s]
#print
delta = max(delta, abs(_udot-Udot[s]) )
Uvec[s] = _uvec
Udot[s] = _udot
if delta < epsilon * (1 - gamma) / gamma:
return Uvec
return Uvec
def prioritized_sweeping_policy_evaluation(self,pi, U1, k=maxint , epsilon=0.001):
"""Return an updated utility mapping U from each state in the MDP to its
utility, using an approximation (modified policy iteration)."""
R, gamma ,expect_vec_u = self.rewards, self.gamma , self.expected_vec_utility
h = []
for s in range(self.nstates):
heappush( h , (-self.rmax-random.uniform(0,1),s) )
print 'after push'
print h
for i in count(0):
U = U1.copy()
(delta,s) = heappop(h)
print 'after pop'
print h
U1[s] = R[s] + gamma * expect_vec_u(s,pi[s],U)
delta = l1distance(U1[s],U[s])
if i > k or delta < epsilon * (1 - gamma) / gamma:
return U
def policy_evaluation(self, epsilon, policy, k, Uvec):
#_Uvec is of dimension nxd
n, d = self.nstates, self.d
gamma , R , expected_scalar_utility = self.gamma , self.rewards , self.expected_scalar_utility
_uvec= np.zeros( (n,d) , dtype=ftype)
for t in range(k):
delta = 0.0
for s in range(n):
# Choose the action
act = random.choice(policy[s])
# Compute the update
_uvec[s] = R[s] + gamma * self.expected_vec_utility(s,act,Uvec)
delta = max(delta, l1distance(_uvec[s] , Uvec[s]) )
for s in range(n):
Uvec[s] = _uvec[s]
if delta < epsilon * (1 - gamma) / gamma:
return Uvec
return Uvec
