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Targeted Opponent Modeling of Memory-Bounded Agents.
Doran
Chakraborty, Noa Agmon, and Peter
Stone.
In Proceedings of the Adaptive Learning Agents Workshop (ALA), May 2013.
[PDF]629.0kB [postscript]1.6MB
In a repeated game, a memory-bounded agent selects its next action by basing its policy on a fixed window of past L plays. Traditionally, approaches that attempt to model memory-bounded agents, do so by modeling them based on the past L joint actions. Since the number of possible L sized joint actions grows exponentially with L, these approaches are restricted to modeling agents with a small L. This paper explores an alternative, more efficient mechanism for modeling memory-bounded agents based on high-level features derived from the past L plays. Called Targeted Opponent Modeler against Memory-Bounded Agents, or TOMMBA, our approach successfully models memory-bounded agents, in a sample efficient manner, given a priori knowledge of a feature set that includes the correct features. TOMMBA is fully implemented, with successful empirical results in a couple of challenging surveillance based tasks.
@InProceedings{ALA13-chakrado,
author = {Doran Chakraborty and Noa Agmon and Peter Stone},
title = {Targeted Opponent Modeling of Memory-Bounded Agents},
booktitle = {Proceedings of the Adaptive Learning Agents Workshop (ALA)},
location = {St. Paul, Minnesota, USA},
month = {May},
year = {2013},
abstract = {
In a repeated game, a memory-bounded agent selects its
next action by basing its policy on a fixed window of
past L plays. Traditionally, approaches that attempt to
model memory-bounded agents, do so by modeling them
based on the past L joint actions. Since the number of
possible L sized joint actions grows exponentially with
L, these approaches are restricted to modeling agents
with a small L. This paper explores an alternative, more
efficient mechanism for modeling memory-bounded agents
based on high-level features derived from the past L
plays. Called Targeted Opponent Modeler against
Memory-Bounded Agents, or TOMMBA, our approach
successfully models memory-bounded agents, in a sample
efficient manner, given a priori knowledge of a feature
set that includes the correct features. TOMMBA is fully
implemented, with successful empirical results in a
couple of challenging surveillance based tasks.
},
}
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