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Zahra Rajabi
pymdptoolbox
Commits
df7f9e55
Commit
df7f9e55
authored
Mar 11, 2014
by
Steven Cordwell
Browse files
fix some typos in docstrings
parent
118a2fa8
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src/mdptoolbox/mdp.py
src/mdptoolbox/mdp.py
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src/mdptoolbox/mdp.py
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df7f9e55
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@@ -70,46 +70,50 @@ class MDP(object):
"""A Markov Decision Problem.
Let
S
= the number of states, and
A
= the number of acions.
Let
``S``
= the number of states, and
``A``
= the number of acions.
Parameters
----------
transitions : array
Transition probability matrices. These can be defined in a variety of
ways. The simplest is a numpy array that has the shape (A, S, S),
ways. The simplest is a numpy array that has the shape
``
(A, S, S)
``
,
though there are other possibilities. It can be a tuple or list or
numpy object array of length A, where each element contains a numpy
array or matrix that has the shape (S, S). This "list of matrices" form
is useful when the transition matrices are sparse as
scipy.sparse.csr_matrix matrices can be used. In summary, each action's
transition matrix must be indexable like ``P[a]`` where
``a`` ∈ {0, 1...A-1}.
numpy object array of length ``A``, where each element contains a numpy
array or matrix that has the shape ``(S, S)``. This "list of matrices"
form is useful when the transition matrices are sparse as
``scipy.sparse.csr_matrix`` matrices can be used. In summary, each
action's transition matrix must be indexable like ``transitions[a]``
where ``a`` ∈ {0, 1...A-1}, and ``transitions[a]`` returns an ``S`` ×
``S`` array-like object.
reward : array
Reward matrices or vectors. Like the transition matrices, these can
also be defined in a variety of ways. Again the simplest is a numpy
array that has the shape (S, A), (S,) or (A, S, S). A list of lists can
be used, where each inner list has length S. A list of numpy arrays is
possible where each inner array can be of the shape (S,), (S, 1),
(1, S) or (S, S). Also scipy.sparse.csr_matrix can be used instead of
numpy arrays. In addition, the outer list can be replaced with a tuple
or numpy object array can be used.
array that has the shape ``(S, A)``, ``(S,)`` or ``(A, S, S)``. A list
of lists can be used, where each inner list has length ``S`` and the
outer list has length ``A``. A list of numpy arrays is possible where
each inner array can be of the shape ``(S,)``, ``(S, 1)``, ``(1, S)``
or ``(S, S)``. Also ``scipy.sparse.csr_matrix`` can be used instead of
numpy arrays. In addition, the outer list can be replaced by any object
that can be indexed like ``reward[a]`` such as a tuple or numpy object
array of length ``A``.
discount : float
Discount factor. The per time-step discount factor on future rewards.
Valid values are greater than 0 upto and including 1. If the discount
factor is 1, then convergence is cannot be assumed and a warning will
be displayed. Subclasses of ``MDP`` may pass None in the case where
the
algorithm does not use a discount factor.
be displayed. Subclasses of ``MDP`` may pass
``
None
``
in the case where
the
algorithm does not use a discount factor.
epsilon : float
Stopping criterion. The maximum change in the value function at each
iteration is compared against ``epsilon``. Once the change falls below
this value, then the value function is considered to have converged to
the optimal value function. Subclasses of ``MDP`` may pass None in the
case where the algorithm does not use a stopping criterion.
the optimal value function. Subclasses of ``MDP`` may pass ``None`` in
the case where the algorithm does not use an epsilon-optimal stopping
criterion.
max_iter : int
Maximum number of iterations. The algorithm will be terminated once
this many iterations have elapsed. This must be greater than 0 if
specified. Subclasses of ``MDP`` may pass None in the case where
the
algorithm does not use a maximum number of iterations.
specified. Subclasses of ``MDP`` may pass
``
None
``
in the case where
the
algorithm does not use a maximum number of iterations.
Attributes
----------
...
...
@@ -130,12 +134,12 @@ class MDP(object):
time : float
The time used to converge to the optimal policy.
verbose : boolean
Whether verbose output should be displayed
in
not.
Whether verbose output should be displayed
or
not.
Methods
-------
run
Implemented in child classes as the main algorithm loop. Raises an
d
Implemented in child classes as the main algorithm loop. Raises an
exception if it has not been overridden.
setSilent
Turn the verbosity off
...
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@@ -314,11 +318,11 @@ class FiniteHorizon(MDP):
---------------
V : array
Optimal value function. Shape = (S, N+1). ``V[:, n]`` = optimal value
function at stage ``n`` with stage in
(
0, 1...N-1
)
. ``V[:, N]`` value
function at stage ``n`` with stage in
{
0, 1...N-1
}
. ``V[:, N]`` value
function for terminal stage.
policy : array
Optimal policy. ``policy[:, n]`` = optimal policy at stage ``n`` with
stage in
(
0, 1...N
)
. ``policy[:, N]`` = policy for stage ``N``.
stage in
{
0, 1...N
}
. ``policy[:, N]`` = policy for stage ``N``.
time : float
used CPU time
...
...
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