Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Interactive Imitation Learning in State-Space   Imitation Learning techniques enable programming the behavior of agents
through demonstrations rather than manual engineering. However, they are
limited by the quality of available demonstration data. Interactive Imitation
Learning techniques can improve the efficacy of learning since they involve
teachers providing feedback while the agent executes its task. In this work, we
propose a novel Interactive Learning technique that uses human feedback in
state-space to train and improve agent behavior (as opposed to alternative
methods that use feedback in action-space). Our method titled Teaching
Imitative Policies in State-space~(TIPS) enables providing guidance to the
agent in terms of `changing its state' which is often more intuitive for a
human demonstrator. Through continuous improvement via corrective feedback,
agents trained by non-expert demonstrators using TIPS outperformed the
demonstrator and conventional Imitation Learning agents.

18
The categories of this paper are: econ
20
The categories of this paper are: acc-phys
32
The categories of this paper are: atom-ph
29
The categories of this paper are: funct-an
34
The categories of this paper are: chem-ph
15
The categories of this paper are: nucl-ex
16
The categories of this paper are: stat
19
The categories of this paper are: eess
26
The categories of this paper are: bayes-an
25
The categories of this paper are: patt-sol
37
The categories of this paper are: supr-con
9
The categories of this paper are: nlin
21
The categories of this paper are: adap-org
12
The categories of this paper are: nucl-th
36
The categories of this paper are: cmp-lg
10
The categories of this paper are: q-bio