Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Avoidance Learning Using Observational Reinforcement Learning   Imitation learning seeks to learn an expert policy from sampled
demonstrations. However, in the real world, it is often difficult to find a
perfect expert and avoiding dangerous behaviors becomes relevant for safety
reasons. We present the idea of \textit{learning to avoid}, an objective
opposite to imitation learning in some sense, where an agent learns to avoid a
demonstrator policy given an environment. We define avoidance learning as the
process of optimizing the agent's reward while avoiding dangerous behaviors
given by a demonstrator. In this work we develop a framework of avoidance
learning by defining a suitable objective function for these problems which
involves the \emph{distance} of state occupancy distributions of the expert and
demonstrator policies. We use density estimates for state occupancy measures
and use the aforementioned distance as the reward bonus for avoiding the
demonstrator. We validate our theory with experiments using a wide range of
partially observable environments. Experimental results show that we are able
to improve sample efficiency during training compared to state of the art
policy optimization and safety methods.

19
The categories of this paper are: eess
14
The categories of this paper are: math-ph
29
The categories of this paper are: funct-an
8
The categories of this paper are: hep-ex
26
The categories of this paper are: bayes-an
0
The categories of this paper are: hep-ph
15
The categories of this paper are: nucl-ex
32
The categories of this paper are: atom-ph
27
The categories of this paper are: comp-gas
37
The categories of this paper are: supr-con
21
The categories of this paper are: adap-org
28
The categories of this paper are: alg-geom
2
The categories of this paper are: math
31
The categories of this paper are: ao-sci
10
The categories of this paper are: q-bio
24
The categories of this paper are: dg-ga