<s>
Self-play	B-Algorithm
is	O
a	O
technique	O
for	O
improving	O
the	O
performance	O
of	O
reinforcement	O
learning	O
agents	O
.	O
</s>
<s>
In	O
multi-agent	B-Algorithm
reinforcement	I-Algorithm
learning	I-Algorithm
experiments	O
,	O
researchers	O
try	O
to	O
optimize	O
the	O
performance	O
of	O
a	O
learning	O
agent	O
on	O
a	O
given	O
task	O
,	O
in	O
cooperation	O
or	O
competition	O
with	O
one	O
or	O
more	O
agents	O
.	O
</s>
<s>
Self-play	B-Algorithm
is	O
used	O
by	O
the	O
AlphaZero	B-Application
program	O
to	O
improve	O
its	O
performance	O
in	O
the	O
games	O
of	O
chess	B-Application
,	O
shogi	O
and	O
go	O
.	O
</s>
<s>
Self-play	B-Algorithm
is	O
also	O
used	O
to	O
train	O
the	O
Cicero	O
AI	O
system	O
to	O
outperform	O
humans	O
at	O
the	O
game	O
of	O
Diplomacy	O
.	O
</s>
<s>
The	O
technique	O
is	O
also	O
used	O
in	O
training	O
the	O
DeepNash	O
system	O
to	O
play	O
the	O
game	O
Stratego	B-Application
.	O
</s>
<s>
Self-Play	B-Algorithm
(	O
SP	O
)	O
:	O
</s>
<s>
Fictitious	O
Self-Play	B-Algorithm
(	O
FSP	O
)	O
:	O
</s>
<s>
Prioritized	O
Fictitious	O
Self-Play	B-Algorithm
(	O
PFSP	O
)	O
:	O
</s>
<s>
Self-play	B-Algorithm
has	O
been	O
compared	O
to	O
the	O
epistemological	O
concept	O
of	O
tabula	B-Application
rasa	I-Application
that	O
describes	O
the	O
way	O
that	O
humans	O
acquire	O
knowledge	O
from	O
a	O
"	O
blank	O
slate	O
"	O
.	O
</s>
