Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Inverse Rational Control with Partially Observable Continuous Nonlinear
  Dynamics   Continuous control and planning remains a major challenge in robotics and
machine learning. Neuroscience offers the possibility of learning from animal
brains that implement highly successful controllers, but it is unclear how to
relate an animal's behavior to control principles. Animals may not always act
optimally from the perspective of an external observer, but may still act
rationally: we hypothesize that animals choose actions with highest expected
future subjective value according to their own internal model of the world.
Their actions thus result from solving a different optimal control problem from
those on which they are evaluated in neuroscience experiments. With this
assumption, we propose a novel framework of model-based inverse rational
control that learns the agent's internal model that best explains their actions
in a task described as a partially observable Markov decision process (POMDP).
In this approach we first learn optimal policies generalized over the entire
model space of dynamics and subjective rewards, using an extended Kalman filter
to represent the belief space, a neural network in the actor-critic framework
to optimize the policy, and a simplified basis for the parameter space. We then
compute the model that maximizes the likelihood of the experimentally
observable data comprising the agent's sensory observations and chosen actions.
Our proposed method is able to recover the true model of simulated agents
within theoretical error bounds given by limited data. We illustrate this
method by applying it to a complex naturalistic task currently used in
neuroscience experiments. This approach provides a foundation for interpreting
the behavioral and neural dynamics of highly adapted controllers in animal
brains.

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The categories of this paper are: hep-lat
37
The categories of this paper are: supr-con
34
The categories of this paper are: chem-ph
4
The categories of this paper are: cond-mat
26
The categories of this paper are: bayes-an
30
The categories of this paper are: q-alg
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The categories of this paper are: patt-sol
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The categories of this paper are: alg-geom
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The categories of this paper are: ao-sci
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The categories of this paper are: physics
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The categories of this paper are: nucl-th
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The categories of this paper are: comp-gas
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The categories of this paper are: nlin
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The categories of this paper are: astro-ph
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The categories of this paper are: adap-org
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The categories of this paper are: solv-int