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Commit 0660caca authored by Steven Cordwell's avatar Steven Cordwell
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completed RelativeValueIteration class to a useful state

parent 4f17fb5f
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mdp.py
View file @ 0660caca
......@@ -304,7 +304,6 @@ def exampleForest(S=3, r1=4, r2=2, p=0.1):
array([[[ 0.1, 0.9, 0. ],
[ 0.1, 0. , 0.9],
[ 0.1, 0. , 0.9]],
[[ 1. , 0. , 0. ],
[ 1. , 0. , 0. ],
[ 1. , 0. , 0. ]]])
......@@ -429,12 +428,12 @@ def getSpan(W):
class MDP(object):
"""The Markov Decision Problem Toolbox."""
def __init__(self, transitions, reward, discount, max_iter):
def __init__(self, transitions, reward, discount, epsilon, max_iter):
""""""
# if the discount is None then the algorithm is assumed to not use it
# in its computations
if (type(discount) is int) or (type(discount) is float):
if type(discount) in (int, float):
if (discount <= 0) or (discount > 1):
raise ValueError(mdperr["discount_rng"])
else:
......@@ -448,8 +447,8 @@ class MDP(object):
# if the max_iter is None then the algorithm is assumed to not use it
# in its computations
if (type(max_iter) is int) or (type(max_iter) is float):
if (max_iter <= 0):
if type(max_iter) in (int, float):
if max_iter <= 0:
raise ValueError(mdperr["maxi_min"])
else:
self.max_iter = max_iter
......@@ -457,6 +456,13 @@ class MDP(object):
raise ValueError("PyMDPtoolbox: max_iter must be a positive real "\
"number greater than zero.")
if type(epsilon) in (int, float):
if epsilon <= 0:
raise ValueError("PyMDPtoolbox: epsilon must be greater than 0")
elif not epsilon is None:
raise ValueError("PyMDPtoolbox: epsilon must be a positive real "\
"number greater than zero.")
# we run a check on P and R to make sure they are describing an MDP. If
# an exception isn't raised then they are assumed to be correct.
check(transitions, reward)
......@@ -744,7 +750,7 @@ class PolicyIteration(MDP):
def __init__(self, transitions, reward, discount, policy0=None, max_iter=1000, eval_type=0):
""""""
MDP.__init__(self, transitions, reward, discount, max_iter)
MDP.__init__(self, transitions, reward, discount, None, max_iter)
if policy0 == None:
# initialise the policy to the one which maximises the expected
......@@ -913,7 +919,7 @@ class PolicyIteration(MDP):
Ppolicy, Rpolicy = self.computePpolicyPRpolicy()
# V = PR + gPV => (I-gP)V = PR => V = inv(I-gP)* PR
self.V = self.lin_eq((self.speye(self.S, self.S) - self.discount * Ppolicy) , Rpolicy)
self.V = self.lin_eq((self.speye(self.S, self.S) - self.discount * Ppolicy), Rpolicy)
def iterate(self):
"""Run the policy iteration algorithm."""
......@@ -961,7 +967,7 @@ class PolicyIteration(MDP):
self.V = tuple(array(self.V).reshape(self.S).tolist())
self.policy = tuple(array(self.policy).reshape(self.S).tolist())
class PolicyIterationModified(MDP):
class PolicyIterationModified(PolicyIteration):
"""Resolution of discounted MDP with policy iteration algorithm
Arguments
......@@ -1002,10 +1008,16 @@ class PolicyIterationModified(MDP):
def __init__(self, transitions, reward, discount, epsilon=0.01, max_iter=10):
""""""
MDP.__init__(self, transitions, reward, discount, max_iter)
PolicyIteration.__init__(self, transitions, reward, discount, None, max_iter, 1)
if epsilon <= 0:
raise ValueError("epsilon must be greater than 0")
# PolicyIteration doesn't pass epsilon to MDP.__init__() so we will
# check it here
if type(epsilon) in (int, float):
if epsilon <= 0:
raise ValueError("PyMDPtoolbox: epsilon must be greater than 0")
else:
raise ValueError("PyMDPtoolbox: epsilon must be a positive real "\
"number greater than zero.")
# computation of threshold of variation for V for an epsilon-optimal policy
if self.discount != 1:
......@@ -1128,7 +1140,7 @@ class QLearning(MDP):
raise ValueError("PyMDPtoolbox: n_iter should be greater than 10000")
# after this n_iter will be known as self.max_iter
MDP.__init__(self, transitions, reward, discount, n_iter)
MDP.__init__(self, transitions, reward, discount, None, n_iter)
# Initialisations
self.Q = zeros((self.S, self.A))
......@@ -1238,13 +1250,15 @@ class RelativeValueIteration(MDP):
def __init__(self, transitions, reward, epsilon=0.01, max_iter=1000):
MDP.__init__(self, transitions, reward, None, max_iter)
MDP.__init__(self, transitions, reward, None, epsilon, max_iter)
if epsilon <= 0:
print('MDP Toolbox ERROR: epsilon must be upper than 0')
self.U = zeros(self.S, 1)
self.gain = self.U[self.S]
self.epsilon = epsilon
self.discount = 1
self.V = matrix(zeros((self.S, 1)))
self.gain = 0 # self.U[self.S]
self.average_reward = None
def iterate(self):
""""""
......@@ -1259,29 +1273,33 @@ class RelativeValueIteration(MDP):
self.iter = self.iter + 1;
Unext, policy = self.bellmanOperator(self.P, self.R, 1, self.U)
Unext = Unext - self.gain
self.policy, Vnext = self.bellmanOperator()
Vnext = Vnext - self.gain
variation = getSpan(Unext - self.U)
variation = getSpan(Vnext - self.V)
if self.verbose:
print(" %s %s" % (self.iter, variation))
if variation < self.epsilon:
done = True
average_reward = self.gain + min(Unext - self.U)
self.average_reward = self.gain + (Vnext - self.V).min()
if self.verbose:
print('MDP Toolbox : iterations stopped, epsilon-optimal policy found')
elif self.iter == self.max_iter:
done = True
average_reward = self.gain + min(Unext - self.U);
self.average_reward = self.gain + (Vnext - self.V).min()
if self.verbose:
print('MDP Toolbox : iterations stopped by maximum number of iteration condition')
self.U = Unext
self.gain = self.U(self.S)
self.V = Vnext
self.gain = float(self.V[self.S - 1])
self.time = time() - self.time
# store value and policy as tuples
self.V = tuple(self.V.getA1().tolist())
self.policy = tuple(self.policy.getA1().tolist())
class ValueIteration(MDP):
"""
......@@ -1401,21 +1419,18 @@ class ValueIteration(MDP):
def __init__(self, transitions, reward, discount, epsilon=0.01, max_iter=1000, initial_value=0):
"""Resolution of discounted MDP with value iteration algorithm."""
MDP.__init__(self, transitions, reward, discount, max_iter)
MDP.__init__(self, transitions, reward, discount, epsilon, max_iter)
# initialization of optional arguments
if (initial_value == 0):
if initial_value == 0:
self.V = matrix(zeros((self.S, 1)))
else:
if (not initial_value.shape in ((self.S, ), (self.S, 1), (1, self.S))):
if not initial_value.shape in ((self.S, ), (self.S, 1), (1, self.S)):
raise ValueError("PyMDPtoolbox: The initial value must be a vector of length S")
else:
self.V = matrix(initial_value)
if epsilon <= 0:
raise ValueError("PyMDPtoolbox: epsilon must be greater than 0")
if (self.discount < 1):
if self.discount < 1:
# compute a bound for the number of iterations and update the
# stored value of self.max_iter
self.boundIter(epsilon)
......@@ -1464,7 +1479,7 @@ class ValueIteration(MDP):
max_iter = log( (epsilon * (1 - self.discount) / self.discount) / getSpan(value - Vprev) ) / log(self.discount * k)
#self.V = Vprev
self.max_iter = ceil(max_iter)
self.max_iter = int(ceil(max_iter))
def iterate(self):
"""
......
test_mdptoolbox.py
View file @ 0660caca
......@@ -6,7 +6,8 @@ Created on Sun May 27 23:16:57 2012
"""
from mdp import check, checkSquareStochastic, exampleForest, exampleRand, MDP
from mdp import PolicyIteration, ValueIteration, ValueIterationGS
from mdp import PolicyIteration, RelativeValueIteration, ValueIteration
from mdp import ValueIterationGS
from numpy import absolute, array, eye, matrix, zeros
from numpy.random import rand
......@@ -18,6 +19,13 @@ STATES = 10
ACTIONS = 3
SMALLNUM = 10e-12
# Arrays
P = array([[[0.5, 0.5],[0.8, 0.2]],[[0, 1],[0.1, 0.9]]])
R = array([[5, 10], [-1, 2]])
Pf, Rf = exampleForest()
Pr, Rr = exampleRand(STATES, ACTIONS)
Prs, Rrs = exampleRand(STATES, ACTIONS, is_sparse=True)
# check: square, stochastic and non-negative ndarrays
def test_check_square_stochastic_nonnegative_array_1():
......@@ -130,7 +138,6 @@ def test_checkSquareStochastic_eye_sparse():
assert checkSquareStochastic(P) == None
# exampleForest
Pf, Rf = exampleForest()
def test_exampleForest_P_shape():
assert (Pf == array([[[0.1, 0.9, 0.0],
......@@ -151,8 +158,6 @@ def test_exampleForest_check():
# exampleRand
Pr, Rr = exampleRand(STATES, ACTIONS)
def test_exampleRand_dense_P_shape():
assert (Pr.shape == (ACTIONS, STATES, STATES))
......@@ -162,8 +167,6 @@ def test_exampleRand_dense_R_shape():
def test_exampleRand_dense_check():
assert check(Pr, Rr) == None
Prs, Rrs = exampleRand(STATES, ACTIONS, is_sparse=True)
def test_exampleRand_sparse_P_shape():
assert (Prs.shape == (ACTIONS, ))
......@@ -173,9 +176,6 @@ def test_exampleRand_sparse_R_shape():
def test_exampleRand_sparse_check():
assert check(Prs, Rrs) == None
P = array([[[0.5, 0.5],[0.8, 0.2]],[[0, 1],[0.1, 0.9]]])
R = array([[5, 10], [-1, 2]])
# MDP
def test_MDP_P_R_1():
......@@ -298,6 +298,11 @@ def test_PolicyIteration_matrix_exampleForest():
# ValueIterationGS
def test_ValueIterationGS_boundIter_exampleForest():
a = ValueIterationGS(Pf, Rf, 0.9)
itr = 39
assert (a.max_iter == itr)
def test_ValueIterationGS_exampleForest():
a = ValueIterationGS(Pf, Rf, 0.9)
p = matrix('0 0 0')
......@@ -308,6 +313,18 @@ def test_ValueIterationGS_exampleForest():
assert a.iter == itr
assert (absolute(array(a.V) - v) < SMALLNUM).all()
# RelativeValueIteration
def test_RelativeValueIteration_exampleForest():
a = RelativeValueIteration(Pf, Rf)
itr = 4
p = matrix('0 0 0')
v = matrix('-4.360000000000000 -0.760000000000000 3.240000000000000')
a.iterate()
assert (array(a.policy) == p).all()
assert a.iter == itr
assert (absolute(array(a.V) - v) < SMALLNUM).all()
#def test_JacksCarRental():
# S = 21 ** 2
# A = 11
......
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