Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Mitigation of Adversarial Policy Imitation via Constrained Randomization
  of Policy (CRoP)   Deep reinforcement learning (DRL) policies are vulnerable to unauthorized
replication attacks, where an adversary exploits imitation learning to
reproduce target policies from observed behavior. In this paper, we propose
Constrained Randomization of Policy (CRoP) as a mitigation technique against
such attacks. CRoP induces the execution of sub-optimal actions at random under
performance loss constraints. We present a parametric analysis of CRoP, address
the optimality of CRoP, and establish theoretical bounds on the adversarial
budget and the expectation of loss. Furthermore, we report the experimental
evaluation of CRoP in Atari environments under adversarial imitation, which
demonstrate the efficacy and feasibility of our proposed method against policy
replication attacks.

5
The categories of this paper are: gr-qc
19
The categories of this paper are: eess
35
The categories of this paper are: mtrl-th
9
The categories of this paper are: nlin
15
The categories of this paper are: nucl-ex
36
The categories of this paper are: cmp-lg
28
The categories of this paper are: alg-geom
31
The categories of this paper are: ao-sci
22
The categories of this paper are: chao-dyn
20
The categories of this paper are: acc-phys
30
The categories of this paper are: q-alg
3
The categories of this paper are: physics
25
The categories of this paper are: patt-sol
12
The categories of this paper are: nucl-th
29
The categories of this paper are: funct-an
7
The categories of this paper are: hep-th