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Zahra Rajabi
pymdptoolbox
Commits
c3202839
Commit
c3202839
authored
May 15, 2013
by
Steven Cordwell
Browse files
added option for sparse transition matrix to be generated from the exampleForest function
parent
b475e0a0
Changes
1
Hide whitespace changes
Inline
Sidebyside
mdp.py
View file @
c3202839
...
...
@@ 100,6 +100,7 @@ from numpy import absolute, array, diag, empty, matrix, mean, mod, multiply
from
numpy
import
ndarray
,
ones
,
zeros
from
numpy.random
import
rand
from
scipy.sparse
import
csr_matrix
as
sparse
from
scipy.sparse
import
coo_matrix
# __all__ = ["check", "checkSquareStochastic"]
...
...
@@ 371,15 +372,15 @@ def checkSquareStochastic(Z):
return
(
None
)
def
exampleForest
(
S
=
3
,
r1
=
4
,
r2
=
2
,
p
=
0.1
):
def
exampleForest
(
S
=
3
,
r1
=
4
,
r2
=
2
,
p
=
0.1
,
is_sparse
=
False
):
"""Generate a MDP example based on a simple forest management scenario.
This function is used to generate a transition probability
(A×S×S) array P
and a reward (S×A) matrix R that model the following problem.
A forest is managed by two actions:
'Wait' and 'Cut'.
An action is decided each year with first the objective
to maintain an old
forest for wildlife and second to make money selling cut
wood.
Each year there is a probability ``p`` that a fire burns the forest.
This function is used to generate a transition probability
(``A`` × ``S`` × ``S``) array ``P`` and a reward (``S`` × ``A``) matrix
``R`` that model the following problem.
A forest is managed by two actions:
'Wait' and 'Cut'.
An action is decided each year with first the objective
to maintain an old
forest for wildlife and second to make money selling cut
wood.
Each year there is a probability ``p`` that a fire burns the forest.
Here is how the problem is modelled.
Let {1, 2 . . . ``S`` } be the states of the forest, with ``S`` being the
...
...
@@ 471,12 +472,27 @@ def exampleForest(S=3, r1=4, r2=2, p=0.1):
#  . . .   . . . 
#  . . 1p   . . . 
#  p 0 0....0 1p   1 0..........0 
P
=
zeros
((
2
,
S
,
S
))
P
[
0
,
:,
:]
=
(
1

p
)
*
diag
(
ones
(
S

1
),
1
)
P
[
0
,
:,
0
]
=
p
P
[
0
,
S

1
,
S

1
]
=
(
1

p
)
P
[
1
,
:,
:]
=
zeros
((
S
,
S
))
P
[
1
,
:,
0
]
=
1
if
is_sparse
:
P
=
[{
"row"
:[],
"col"
:[],
"val"
:[]},
{
"row"
:[],
"col"
:[],
"val"
:[]}]
P
[
0
][
"row"
]
=
range
(
S
)
*
2
P
[
0
][
"col"
]
=
[
0
]
*
S
P
[
0
][
"col"
].
extend
(
range
(
1
,
S
))
P
[
0
][
"col"
].
append
(
S

1
)
P
[
0
][
"val"
]
=
[
0.1
]
*
S
P
[
0
][
"val"
].
extend
([
0.9
]
*
S
)
P
[
1
][
"row"
]
=
range
(
S
)
P
[
1
][
"col"
]
=
[
0
]
*
S
P
[
1
][
"val"
]
=
[
1
]
*
S
for
x
in
range
(
2
):
P
[
x
]
=
coo_matrix
((
P
[
x
][
"val"
],
(
P
[
x
][
"row"
],
P
[
x
][
"col"
])),
shape
=
(
S
,
S
)).
tocsr
()
else
:
P
=
zeros
((
2
,
S
,
S
))
P
[
0
,
:,
:]
=
(
1

p
)
*
diag
(
ones
(
S

1
),
1
)
P
[
0
,
:,
0
]
=
p
P
[
0
,
S

1
,
S

1
]
=
(
1

p
)
P
[
1
,
:,
:]
=
zeros
((
S
,
S
))
P
[
1
,
:,
0
]
=
1
# Definition of Reward matrix R1 associated to action Wait and
# R2 associated to action Cut
#  0   0 
...
...
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