Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Value-of-Information based Arbitration between Model-based and
  Model-free Control   There have been numerous attempts in explaining the general learning
behaviours using model-based and model-free methods. While the model-based
control is flexible yet computationally expensive in planning, the model-free
control is quick but inflexible. The model-based control is therefore immune
from reward devaluation and contingency degradation. Multiple arbitration
schemes have been suggested to achieve the data efficiency and computational
efficiency of model-based and model-free control respectively. In this context,
we propose a quantitative 'value of information' based arbitration between both
the controllers in order to establish a general computational framework for
skill learning. The interacting model-based and model-free reinforcement
learning processes are arbitrated using an uncertainty-based value of
information. We further show that our algorithm performs better than Q-learning
as well as Q-learning with experience replay.

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The categories of this paper are: eess
22
The categories of this paper are: chao-dyn
33
The categories of this paper are: plasm-ph
14
The categories of this paper are: math-ph
17
The categories of this paper are: q-fin
30
The categories of this paper are: q-alg
13
The categories of this paper are: quant-ph
28
The categories of this paper are: alg-geom
5
The categories of this paper are: gr-qc
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The categories of this paper are: hep-lat
4
The categories of this paper are: cond-mat
16
The categories of this paper are: stat
31
The categories of this paper are: ao-sci
18
The categories of this paper are: econ
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The categories of this paper are: solv-int
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The categories of this paper are: patt-sol