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<font color='#009900'>// Copyright (C) 2015 Davis E. King (davis@dlib.net)
</font><font color='#009900'>// License: Boost Software License See LICENSE.txt for the full license.
</font><font color='#0000FF'>#undef</font> DLIB_LSPI_ABSTRACT_Hh_
<font color='#0000FF'>#ifdef</font> DLIB_LSPI_ABSTRACT_Hh_
<font color='#0000FF'>#include</font> "<a style='text-decoration:none' href='approximate_linear_models_abstract.h.html'>approximate_linear_models_abstract.h</a>"
<font color='#0000FF'>namespace</font> dlib
<b>{</b>
<font color='#009900'>// ----------------------------------------------------------------------------------------
</font>
<font color='#0000FF'>template</font> <font color='#5555FF'><</font>
<font color='#0000FF'>typename</font> feature_extractor
<font color='#5555FF'>></font>
<font color='#0000FF'>class</font> <b><a name='lspi'></a>lspi</b>
<b>{</b>
<font color='#009900'>/*!
REQUIREMENTS ON feature_extractor
feature_extractor should implement the example_feature_extractor interface
defined at the top of dlib/control/approximate_linear_models_abstract.h
WHAT THIS OBJECT REPRESENTS
This object is an implementation of the reinforcement learning algorithm
described in the following paper:
Lagoudakis, Michail G., and Ronald Parr. "Least-squares policy
iteration." The Journal of Machine Learning Research 4 (2003):
1107-1149.
This means that it takes a bunch of training data in the form of
process_samples and outputs a policy that hopefully performs well when run
on the process that generated those samples.
!*/</font>
<font color='#0000FF'>public</font>:
<font color='#0000FF'>typedef</font> feature_extractor feature_extractor_type;
<font color='#0000FF'>typedef</font> <font color='#0000FF'>typename</font> feature_extractor::state_type state_type;
<font color='#0000FF'>typedef</font> <font color='#0000FF'>typename</font> feature_extractor::action_type action_type;
<font color='#0000FF'>explicit</font> <b><a name='lspi'></a>lspi</b><font face='Lucida Console'>(</font>
<font color='#0000FF'>const</font> feature_extractor<font color='#5555FF'>&</font> fe_
<font face='Lucida Console'>)</font>;
<font color='#009900'>/*!
ensures
- #get_feature_extractor() == fe_
- #get_lambda() == 0.01
- #get_discount == 0.8
- #get_epsilon() == 0.01
- is not verbose
- #get_max_iterations() == 100
!*/</font>
<b><a name='lspi'></a>lspi</b><font face='Lucida Console'>(</font>
<font face='Lucida Console'>)</font>;
<font color='#009900'>/*!
ensures
- #get_feature_extractor() == feature_extractor()
(i.e. it will have its default value)
- #get_lambda() == 0.01
- #get_discount == 0.8
- #get_epsilon() == 0.01
- is not verbose
- #get_max_iterations() == 100
!*/</font>
<font color='#0000FF'><u>double</u></font> <b><a name='get_discount'></a>get_discount</b> <font face='Lucida Console'>(</font>
<font face='Lucida Console'>)</font> <font color='#0000FF'>const</font>;
<font color='#009900'>/*!
ensures
- returns the discount applied to the sum of rewards in the Bellman
equation.
!*/</font>
<font color='#0000FF'><u>void</u></font> <b><a name='set_discount'></a>set_discount</b> <font face='Lucida Console'>(</font>
<font color='#0000FF'><u>double</u></font> value
<font face='Lucida Console'>)</font>;
<font color='#009900'>/*!
requires
- 0 < value <= 1
ensures
- #get_discount() == value
!*/</font>
<font color='#0000FF'>const</font> feature_extractor<font color='#5555FF'>&</font> <b><a name='get_feature_extractor'></a>get_feature_extractor</b> <font face='Lucida Console'>(</font>
<font face='Lucida Console'>)</font> <font color='#0000FF'>const</font>;
<font color='#009900'>/*!
ensures
- returns the feature extractor used by this object
!*/</font>
<font color='#0000FF'><u>void</u></font> <b><a name='be_verbose'></a>be_verbose</b> <font face='Lucida Console'>(</font>
<font face='Lucida Console'>)</font>;
<font color='#009900'>/*!
ensures
- This object will print status messages to standard out so that a
user can observe the progress of the algorithm.
!*/</font>
<font color='#0000FF'><u>void</u></font> <b><a name='be_quiet'></a>be_quiet</b> <font face='Lucida Console'>(</font>
<font face='Lucida Console'>)</font>;
<font color='#009900'>/*!
ensures
- this object will not print anything to standard out
!*/</font>
<font color='#0000FF'><u>void</u></font> <b><a name='set_epsilon'></a>set_epsilon</b> <font face='Lucida Console'>(</font>
<font color='#0000FF'><u>double</u></font> eps
<font face='Lucida Console'>)</font>;
<font color='#009900'>/*!
requires
- eps > 0
ensures
- #get_epsilon() == eps
!*/</font>
<font color='#0000FF'><u>double</u></font> <b><a name='get_epsilon'></a>get_epsilon</b> <font face='Lucida Console'>(</font>
<font face='Lucida Console'>)</font> <font color='#0000FF'>const</font>;
<font color='#009900'>/*!
ensures
- returns the error epsilon that determines when training should stop.
Smaller values may result in a more accurate solution but take longer to
train.
!*/</font>
<font color='#0000FF'><u>void</u></font> <b><a name='set_lambda'></a>set_lambda</b> <font face='Lucida Console'>(</font>
<font color='#0000FF'><u>double</u></font> lambda_
<font face='Lucida Console'>)</font>;
<font color='#009900'>/*!
requires
- lambda >= 0
ensures
- #get_lambda() == lambda
!*/</font>
<font color='#0000FF'><u>double</u></font> <b><a name='get_lambda'></a>get_lambda</b> <font face='Lucida Console'>(</font>
<font face='Lucida Console'>)</font> <font color='#0000FF'>const</font>;
<font color='#009900'>/*!
ensures
- returns the regularization parameter. It is the parameter that
determines the trade off between trying to fit the training data
exactly or allowing more errors but hopefully improving the
generalization ability of the resulting function. Smaller values
encourage exact fitting while larger values of lambda may encourage
better generalization.
!*/</font>
<font color='#0000FF'><u>void</u></font> <b><a name='set_max_iterations'></a>set_max_iterations</b> <font face='Lucida Console'>(</font>
<font color='#0000FF'><u>unsigned</u></font> <font color='#0000FF'><u>long</u></font> max_iter
<font face='Lucida Console'>)</font>;
<font color='#009900'>/*!
ensures
- #get_max_iterations() == max_iter
!*/</font>
<font color='#0000FF'><u>unsigned</u></font> <font color='#0000FF'><u>long</u></font> <b><a name='get_max_iterations'></a>get_max_iterations</b> <font face='Lucida Console'>(</font>
<font face='Lucida Console'>)</font>;
<font color='#009900'>/*!
ensures
- returns the maximum number of iterations the SVM optimizer is allowed to
run before it is required to stop and return a result.
!*/</font>
<font color='#0000FF'>template</font> <font color='#5555FF'><</font>
<font color='#0000FF'>typename</font> vector_type
<font color='#5555FF'>></font>
policy<font color='#5555FF'><</font>feature_extractor<font color='#5555FF'>></font> <b><a name='train'></a>train</b> <font face='Lucida Console'>(</font>
<font color='#0000FF'>const</font> vector_type<font color='#5555FF'>&</font> samples
<font face='Lucida Console'>)</font> <font color='#0000FF'>const</font>;
<font color='#009900'>/*!
requires
- samples.size() > 0
- samples is something with an interface that looks like
std::vector<process_sample<feature_extractor>>. That is, it should
be some kind of array of process_sample objects.
ensures
- Trains a policy based on the given data and returns the results. The
idea is to find a policy that will obtain the largest possible reward
when run on the process that generated the samples. In particular,
if the returned policy is P then:
- P(S) == the best action to take when in state S.
- if (feature_extractor::force_last_weight_to_1) then
- The last element of P.get_weights() is 1.
!*/</font>
<b>}</b>;
<font color='#009900'>// ----------------------------------------------------------------------------------------
</font>
<b>}</b>
<font color='#0000FF'>#endif</font> <font color='#009900'>// DLIB_LSPI_ABSTRACT_Hh_
</font>
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