Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Variational Bayesian Reinforcement Learning with Regret Bounds   In reinforcement learning the Q-values summarize the expected future rewards
that the agent will attain. However, they cannot capture the epistemic
uncertainty about those rewards. In this work we derive a new Bellman operator
with associated fixed point we call the `knowledge values'. These K-values
compress both the expected future rewards and the epistemic uncertainty into a
single value, so that high uncertainty, high reward, or both, can yield high
K-values. The key principle is to endow the agent with a risk-seeking utility
function that is carefully tuned to balance exploration and exploitation. When
the agent follows a Boltzmann policy over the K-values it yields a Bayes regret
bound of $\tilde O(L^{3/2} \sqrt{S A T})$, where $L$ is the time horizon, $S$
is the number of states, $A$ is the number of actions, and $T$ is the total
number of elapsed timesteps. We show deep connections of this approach to the
soft-max and maximum-entropy strands of research in reinforcement learning.

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The categories of this paper are: nlin
27
The categories of this paper are: comp-gas
25
The categories of this paper are: patt-sol
10
The categories of this paper are: q-bio
37
The categories of this paper are: supr-con
12
The categories of this paper are: nucl-th
4
The categories of this paper are: cond-mat
32
The categories of this paper are: atom-ph
21
The categories of this paper are: adap-org
18
The categories of this paper are: econ
2
The categories of this paper are: math
33
The categories of this paper are: plasm-ph
34
The categories of this paper are: chem-ph
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The categories of this paper are: hep-lat
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The categories of this paper are: alg-geom
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The categories of this paper are: mtrl-th