7,018 research outputs found
Bayesian Modeling of a Human MMORPG Player
This paper describes an application of Bayesian programming to the control of
an autonomous avatar in a multiplayer role-playing game (the example is based
on World of Warcraft). We model a particular task, which consists of choosing
what to do and to select which target in a situation where allies and foes are
present. We explain the model in Bayesian programming and show how we could
learn the conditional probabilities from data gathered during human-played
sessions.Comment: 30th international workshop on Bayesian Inference and Maximum
Entropy, Chamonix : France (2010
Learning Parameterized Skills
We introduce a method for constructing skills capable of solving tasks drawn
from a distribution of parameterized reinforcement learning problems. The
method draws example tasks from a distribution of interest and uses the
corresponding learned policies to estimate the topology of the
lower-dimensional piecewise-smooth manifold on which the skill policies lie.
This manifold models how policy parameters change as task parameters vary. The
method identifies the number of charts that compose the manifold and then
applies non-linear regression in each chart to construct a parameterized skill
by predicting policy parameters from task parameters. We evaluate our method on
an underactuated simulated robotic arm tasked with learning to accurately throw
darts at a parameterized target location.Comment: Appears in Proceedings of the 29th International Conference on
Machine Learning (ICML 2012
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