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AAAI21_Emergent_language
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haoyifan
AAAI21_Emergent_language
Commits
e019c58d
Commit
e019c58d
authored
Sep 08, 2020
by
Ruizhi Chen
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使用package algorithm,将算法伪代码添加到paper.tex中。
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AAAI2021/paper.tex
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e019c58d
...
@@ -21,6 +21,8 @@
...
@@ -21,6 +21,8 @@
\frenchspacing
% DO NOT CHANGE THIS
\frenchspacing
% DO NOT CHANGE THIS
\setlength
{
\pdfpagewidth
}{
8.5in
}
% DO NOT CHANGE THIS
\setlength
{
\pdfpagewidth
}{
8.5in
}
% DO NOT CHANGE THIS
\setlength
{
\pdfpageheight
}{
11in
}
% DO NOT CHANGE THIS
\setlength
{
\pdfpageheight
}{
11in
}
% DO NOT CHANGE THIS
\usepackage
{
algorithm
}
\usepackage
{
algorithmic
}
%\nocopyright
%\nocopyright
%PDF Info Is REQUIRED.
%PDF Info Is REQUIRED.
% For /Author, add all authors within the parentheses, separated by commas. No accents or commands.
% For /Author, add all authors within the parentheses, separated by commas. No accents or commands.
...
@@ -191,6 +193,42 @@
...
@@ -191,6 +193,42 @@
\input
{
tex/experiments.tex
}
\input
{
tex/experiments.tex
}
\input
{
tex/last.tex
}
\input
{
tex/last.tex
}
\begin{algorithm}
[!h]
\caption
{
OurAlgorithm
$
(
t,
\hat
{
t
}
)
$}
\begin{algorithmic}
[1]
\IF
{
Training the speaker agent S
}
\FOR
{
Batch T randomly selected from
$
M
_
0
\times
M
_
1
$}
\FOR
{$
t
=(
c
_
0
,c
_
1
)
$
in T
}
\STATE
$
P
(
s
_
0
|t
)
,P
(
s
_
1
|t
)=
\pi
_{
old
}^
S
(
s
=(
s
_
0
,s
_
1
)
|t
)
$
\STATE
Sample
$
s
_
0
$
with
$
P
(
s
_
0
|t
)
$
,
$
s
_
1
$
with
$
P
(
s
_
1
|t
)
$
\STATE
$
P
(
\hat
{
t
}
|s
)
=
\pi
^
L
(
\hat
{
t
}
|s
)
$
\STATE
Sample
$
\hat
{
t
}$
with
$
P
(
\hat
{
t
}
|s
)
$
\STATE
Get reward
$
R
(
\hat
{
t
}
,t
)
$
\STATE
$
J
(
\theta
^
S,
\theta
^
L
)=
E
_{
\pi
_{
old
}^
S,
\pi
^
L
}
[
R
(
\hat
{
t
}
,t
)
\cdot\frac
{
\pi
^
S
(
s|t
)
}{
\pi
^
S
_{
old
}
(
s|t
)
}
]
$
\STATE
Update
$
\theta
^
S
$
by
$
\bigtriangledown
_{
\theta
^
S
}
J
$
\ENDFOR
\STATE
$
\pi
_{
old
}^
S
\leftarrow
\pi
^
S
$
\ENDFOR
\ENDIF
\IF
{
Training the listener agent L
}
\FOR
{
Batch T randomly selected from
$
M
_
0
\times
M
_
1
$}
\FOR
{$
t
=(
c
_
0
,c
_
1
)
$
in T
}
\STATE
$
P
(
s
_
0
|t
)
,P
(
s
_
1
|t
)=
\pi
^
S
(
s
=(
s
_
0
,s
_
1
)
|t
)
$
\STATE
Sample
$
s
_
0
$
with
$
P
(
s
_
0
|t
)
$
,
$
s
_
1
$
with
$
P
(
s
_
1
|t
)
$
\STATE
$
P
(
\hat
{
t
}
|s
)
=
\pi
^
L
_{
old
}
(
\hat
{
t
}
|s
)
$
\STATE
Sample
$
\hat
{
t
}$
with
$
P
(
\hat
{
t
}
|s
)
$
\STATE
Get reward
$
R
(
\hat
{
t
}
,t
)
$
\STATE
$
J
(
\theta
^
S,
\theta
^
L
)=
E
_{
\pi
_{
old
}^
S,
\pi
^
L
}
[
R
(
\hat
{
t
}
,t
)
\cdot\frac
{
\pi
^
L
(
s|t
)
}{
\pi
^
L
_{
old
}
(
s|t
)
}
]
$
\STATE
Update
$
\theta
^
L
$
by
$
\bigtriangledown
_{
\theta
^
L
}
J
$
\ENDFOR
\STATE
$
\pi
_{
old
}^
L
\leftarrow
\pi
^
L
$
\ENDFOR
\ENDIF
\end{algorithmic}
\end{algorithm}
\bibliography
{
ref.bib
}
\bibliography
{
ref.bib
}
\end{document}
\end{document}
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