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Commit 361b42e0 authored by Steven Cordwell's avatar Steven Cordwell
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changed PolicyIteration to be more like original

parent 60e89bf1
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mdp.py
View file @ 361b42e0
......@@ -699,68 +699,54 @@ class PolicyIteration(MDP):
"""
def __init__(self, transitions, reward, discount, epsilon=0.01, max_iter=1000, initial_value=0):
def __init__(self, transitions, reward, discount, policy0, max_iter=1000, eval_type=0):
""""""
MDP.__init__(self)
self.check(transitions, reward)
self.S = transitions.shape[1]
self.A = transitions.shape[0]
self.P = transitions
if (size(policy0,1) != S or any(mod(policy0, 1)) or any(policy0 < 1) or any(policy0 > S)):
raise ValueError('MDP Toolbox ERROR: policy0 must a (1xS) vector with integer from 1 to S')
self.R = reward
#self.computePR(transitions, reward)
if (initial_value == 0):
self.value = zeros((self.S))
#self.value = matrix(zeros((self.S, 1)))
else:
if (len(initial_value) != self.S):
raise ValueError("The initial value must be length S")
self.value = matrix(initial_value)
MDP.__init__(self, transitions, reward, discount, max_iter)
self.policy = randi(0, self.A, self.S)
self.discount = discount
self.max_iter = max_iter
self.value = matrix(zeros((self.S, 1)))
self.iter = 0
# initialise the policy to the one which maximises the expected
# immediate reward
self.bellmanOperator()
def evalPolicyMatrix(self):
""""""
pass
def iterate(self):
""""""
done = False
stop_criterion = 0.01
if self.verbose:
print(' Iteration Number_of_different_actions')
self.time = time()
while not done:
stop = False
while not stop:
change = 0
for s in range(self.S):
v = self.value[s]
a = self.policy[s]
self.value[s] = (self.P[a, s, :] * (self.R[a, s, :] +
(self.discount * self.value))).sum()
change = max(change, abs(v - self.value[s]))
if change < stop_criterion:
stop = True
self.iter = self.iter + 1
policy_stable = True
for s in range(self.S):
b = self.policy[s]
self.policy[s] = (self.P[:, s, :] * (self.R[:, s, :] +
(self.discount * self.value))).sum(1).argmax()
if b != self.policy[s]:
policy_stable = False
if eval_type == 0:
self.value = self.evalPolicyMatrix()
else:
self.value = self.evalPolicyIterative()
if policy_stable:
policy_prev = self.policy
self.bellmanOperator()
n_different = (policy != policy_prev).sum()
if self.verbose:
print(' %s %s') % (self.iter, n_different)
if (policy == policy_prev).all() or (self.iter == self.max_iter):
done = True
self.time = time() - self.time
# store value and policy as tuples
self.value = tuple(array(self.value).reshape(self.S).tolist())
self.policy = tuple(array(self.policy).reshape(self.S).tolist())
......
test_mdptoolbox.py
View file @ 361b42e0
......@@ -5,89 +5,90 @@ Created on Sun May 27 23:16:57 2012
@author: -
"""
from mdp import exampleForest, exampleRand, MDP, PolicyIteration, ValueIteration
from numpy import array, eye, matrix, zeros
from numpy.random import rand
from scipy.sparse import eye as speye
from scipy.sparse import csr_matrix as sparse
from mdp import exampleForest, exampleRand, PolicyIteration, ValueIteration
from numpy import array
#from numpy import array, eye, matrix, zeros
#from numpy.random import rand
#from scipy.sparse import eye as speye
#from scipy.sparse import csr_matrix as sparse
#from scipy.stats.distributions import poisson
inst = MDP()
#inst = MDP()
#
STATES = 10
ACTIONS = 3
# check: square, stochastic and non-negative
def test_check_square_stochastic_nonnegative_array():
P = zeros((ACTIONS, STATES, STATES))
R = zeros((STATES, ACTIONS))
for a in range(ACTIONS):
P[a, :, :] = eye(STATES)
R[:, a] = rand(STATES)
inst.check(P, R)
# check: square, stochastic and non-negative object arrays
def test_check_square_stochastic_nonnegative_object_array():
P = zeros((ACTIONS, ), dtype=object)
R = zeros((STATES, ACTIONS))
for a in range(ACTIONS):
P[a] = eye(STATES)
R[:, a] = rand(STATES)
inst.check(P, R)
def test_check_square_stochastic_nonnegative_object_matrix():
P = zeros((ACTIONS, ), dtype=object)
R = zeros((STATES, ACTIONS))
for a in range(ACTIONS):
P[a] = matrix(eye(STATES))
R[:, a] = rand(STATES)
inst.check(P, R)
def test_check_square_stochastic_nonnegative_object_sparse():
P = zeros((ACTIONS, ), dtype=object)
R = zeros((STATES, ACTIONS))
for a in range(ACTIONS):
P[a] = speye(STATES, STATES).tocsr()
R[:, a] = rand(STATES)
inst.check(P, R)
# checkSquareStochastic: square, stochastic and non-negative
def test_checkSquareStochastic_square_stochastic_nonnegative_array():
P = rand(STATES, STATES)
for s in range(STATES):
P[s, :] = P[s, :] / P[s, :].sum()
assert inst.checkSquareStochastic(P) == None
def test_checkSquareStochastic_square_stochastic_nonnegative_matrix():
P = rand(STATES, STATES)
for s in range(STATES):
P[s, :] = P[s, :] / P[s, :].sum()
P = matrix(P)
assert inst.checkSquareStochastic(P) == None
def test_checkSquareStochastic_square_stochastic_nonnegative_sparse():
P = rand(STATES, STATES)
for s in range(STATES):
P[s, :] = P[s, :] / P[s, :].sum()
P = sparse(P)
assert inst.checkSquareStochastic(P) == None
# checkSquareStochastic: eye
def test_checkSquareStochastic_eye_array():
P = eye(STATES)
assert inst.checkSquareStochastic(P) == None
def test_checkSquareStochastic_eye_matrix():
P = matrix(eye(STATES))
assert inst.checkSquareStochastic(P) == None
def test_checkSquareStochastic_eye_sparse():
P = speye(STATES, STATES).tocsr()
assert inst.checkSquareStochastic(P) == None
#
## check: square, stochastic and non-negative
#
#def test_check_square_stochastic_nonnegative_array():
# P = zeros((ACTIONS, STATES, STATES))
# R = zeros((STATES, ACTIONS))
# for a in range(ACTIONS):
# P[a, :, :] = eye(STATES)
# R[:, a] = rand(STATES)
# inst.check(P, R)
#
## check: square, stochastic and non-negative object arrays
#
#def test_check_square_stochastic_nonnegative_object_array():
# P = zeros((ACTIONS, ), dtype=object)
# R = zeros((STATES, ACTIONS))
# for a in range(ACTIONS):
# P[a] = eye(STATES)
# R[:, a] = rand(STATES)
# inst.check(P, R)
#
#def test_check_square_stochastic_nonnegative_object_matrix():
# P = zeros((ACTIONS, ), dtype=object)
# R = zeros((STATES, ACTIONS))
# for a in range(ACTIONS):
# P[a] = matrix(eye(STATES))
# R[:, a] = rand(STATES)
# inst.check(P, R)
#
#def test_check_square_stochastic_nonnegative_object_sparse():
# P = zeros((ACTIONS, ), dtype=object)
# R = zeros((STATES, ACTIONS))
# for a in range(ACTIONS):
# P[a] = speye(STATES, STATES).tocsr()
# R[:, a] = rand(STATES)
# inst.check(P, R)
#
## checkSquareStochastic: square, stochastic and non-negative
#
#def test_checkSquareStochastic_square_stochastic_nonnegative_array():
# P = rand(STATES, STATES)
# for s in range(STATES):
# P[s, :] = P[s, :] / P[s, :].sum()
# assert inst.checkSquareStochastic(P) == None
#
#def test_checkSquareStochastic_square_stochastic_nonnegative_matrix():
# P = rand(STATES, STATES)
# for s in range(STATES):
# P[s, :] = P[s, :] / P[s, :].sum()
# P = matrix(P)
# assert inst.checkSquareStochastic(P) == None
#
#def test_checkSquareStochastic_square_stochastic_nonnegative_sparse():
# P = rand(STATES, STATES)
# for s in range(STATES):
# P[s, :] = P[s, :] / P[s, :].sum()
# P = sparse(P)
# assert inst.checkSquareStochastic(P) == None
#
## checkSquareStochastic: eye
#
#def test_checkSquareStochastic_eye_array():
# P = eye(STATES)
# assert inst.checkSquareStochastic(P) == None
#
#def test_checkSquareStochastic_eye_matrix():
# P = matrix(eye(STATES))
# assert inst.checkSquareStochastic(P) == None
#
#def test_checkSquareStochastic_eye_sparse():
# P = speye(STATES, STATES).tocsr()
# assert inst.checkSquareStochastic(P) == None
# exampleForest
......@@ -103,9 +104,9 @@ def test_exampleForest_shape():
[0, 1],
[4, 2]])).all()
def test_exampleForest_check():
P, R = exampleForest(10, 5, 3, 0.2)
inst.check(P, R)
#def test_exampleForest_check():
# P, R = exampleForest(10, 5, 3, 0.2)
# inst.check(P, R)
# exampleRand
......@@ -114,18 +115,18 @@ def test_exampleRand_dense_shape():
assert (P.shape == (ACTIONS, STATES, STATES))
assert (R.shape == (ACTIONS, STATES, STATES))
def test_exampleRand_dense_check():
P, R = exampleRand(STATES, ACTIONS)
assert inst.check(P, R) == None
#def test_exampleRand_dense_check():
# P, R = exampleRand(STATES, ACTIONS)
# assert inst.check(P, R) == None
def test_exampleRand_sparse_shape():
P, R = exampleRand(STATES, ACTIONS, is_sparse=True)
assert (P.shape == (ACTIONS, ))
assert (R.shape == (ACTIONS, ))
def test_exampleRand_sparse_check():
P, R = exampleRand(STATES, ACTIONS, is_sparse=True)
assert inst.check(P, R) == None
#def test_exampleRand_sparse_check():
# P, R = exampleRand(STATES, ACTIONS, is_sparse=True)
# assert inst.check(P, R) == None
# ValueIteration
......
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