Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: ReNeg and Backseat Driver: Learning from Demonstration with Continuous
  Human Feedback   In autonomous vehicle (AV) control, allowing mistakes can be quite dangerous
and costly in the real world. For this reason we investigate methods of
training an AV without allowing the agent to explore and instead having a human
explorer collect the data. Supervised learning has been explored for AV
control, but it encounters the issue of the covariate shift. That is, training
data collected from an optimal demonstration consists only of the states
induced by the optimal control policy, but at runtime, the trained agent may
encounter a vastly different state distribution with little relevant training
data. To mitigate this issue, we have our human explorer make sub-optimal
decisions. In order to have our agent not replicate these sub-optimal
decisions, supervised learning requires that we either erase these actions, or
replace these action with the correct action. Erasing is wasteful and replacing
is difficult, since it is not easy to know the correct action without driving.
We propose an alternate framework that includes continuous scalar feedback for
each action, marking which actions we should replicate, which we should avoid,
and how sure we are. Our framework learns continuous control from sub-optimal
demonstration and evaluative feedback collected before training. We find that a
human demonstrator can explore sub-optimal states in a safe manner, while still
getting enough gradation to benefit learning. The collection method for data
and feedback we call "Backseat Driver." We call the more general learning
framework ReNeg, since it learns a regression from states to actions given
negative as well as positive examples. We empirically validate several models
in the ReNeg framework, testing on lane-following with limited data. We find
that the best solution is a generalization of mean-squared error and
outperforms supervised learning on the positive examples alone.

36
The categories of this paper are: cmp-lg
3
The categories of this paper are: physics
35
The categories of this paper are: mtrl-th
18
The categories of this paper are: econ
10
The categories of this paper are: q-bio
26
The categories of this paper are: bayes-an
14
The categories of this paper are: math-ph
24
The categories of this paper are: dg-ga
37
The categories of this paper are: supr-con
20
The categories of this paper are: acc-phys
25
The categories of this paper are: patt-sol
9
The categories of this paper are: nlin
23
The categories of this paper are: solv-int
4
The categories of this paper are: cond-mat
19
The categories of this paper are: eess
31
The categories of this paper are: ao-sci