Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Local Differential Privacy for Regret Minimization in Reinforcement
  Learning   Reinforcement learning algorithms are widely used in domains where it is
desirable to provide a personalized service. In these domains it is common that
user data contains sensitive information that needs to be protected from third
parties. Motivated by this, we study privacy in the context of finite-horizon
Markov Decision Processes (MDPs) by requiring information to be obfuscated on
the user side. We formulate this notion of privacy for RL by leveraging the
local differential privacy (LDP) framework. We establish a lower bound for
regret minimization in finite-horizon MDPs with LDP guarantees which shows that
guaranteeing privacy has a multiplicative effect on the regret. This result
shows that while LDP is an appealing notion of privacy, it makes the learning
problem significantly more complex. Finally, we present an optimistic algorithm
that simultaneously satisfies $\varepsilon$-LDP requirements, and achieves
$\sqrt{K}/\varepsilon$ regret in any finite-horizon MDP after $K$ episodes,
matching the lower bound dependency on the number of episodes $K$.

14
The categories of this paper are: math-ph
32
The categories of this paper are: atom-ph
7
The categories of this paper are: hep-th
12
The categories of this paper are: nucl-th
19
The categories of this paper are: eess
10
The categories of this paper are: q-bio
22
The categories of this paper are: chao-dyn
28
The categories of this paper are: alg-geom
27
The categories of this paper are: comp-gas
33
The categories of this paper are: plasm-ph
35
The categories of this paper are: mtrl-th
16
The categories of this paper are: stat
9
The categories of this paper are: nlin
5
The categories of this paper are: gr-qc
3
The categories of this paper are: physics
31
The categories of this paper are: ao-sci