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General Game Learning using Knowledge Transfer.
Bikramjit Banerjee
and Peter Stone.
In The 20th International Joint Conference on Artificial
Intelligence, pp. 672–677, January 2007.
IJCAI-07
[PDF]118.1kB [postscript]198.5kB
We present a reinforcement learning game player that can interact with a General Game Playing system and transfer knowledge learned in one game to expedite learning in many other games. We use the technique of value-function transfer where general features are extracted from the state space of a previous game and matched with the completely different state space of a new game. To capture the underlying similarity of vastly disparate state spaces arising from different games, we use a game-tree lookahead structure for features. We show that such feature-based value function transfer learns superior policies faster than a reinforcement learning agent that does not use knowledge transfer. Furthermore, knowledge transfer using lookahead features can capture opponent-specific value-functions, i.e. can exploit an opponent's weaknesses to learn faster than a reinforcement learner that uses lookahead with minimax (pessimistic) search against the same opponent.
@InProceedings(IJCAI07-bikram,
author="Bikramjit Banerjee and Peter Stone",
title="General Game Learning using Knowledge Transfer",
BookTitle="The 20th International Joint Conference on Artificial Intelligence",
month="January",year="2007",
pages="672--677",
abstract="
We present a reinforcement learning game player that
can interact with a General Game Playing system and
transfer knowledge learned in one game to expedite
learning in many other games. We use the technique
of value-function transfer where general features
are extracted from the state space of a previous
game and matched with the completely different state
space of a new game. To capture the underlying
similarity of vastly disparate state spaces arising
from different games, we use a game-tree lookahead
structure for features. We show that such
feature-based value function transfer learns
superior policies faster than a reinforcement
learning agent that does not use knowledge
transfer. Furthermore, knowledge transfer using
lookahead features can capture opponent-specific
value-functions, i.e. can exploit an opponent's
weaknesses to learn faster than a reinforcement
learner that uses lookahead with minimax
(pessimistic) search against the same
opponent.
",
wwwnote={<a href="http://www.ijcai-07.org/">IJCAI-07</a>},
)
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