Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning to Fly via Deep Model-Based Reinforcement Learning   Learning to control robots without requiring engineered models has been a
long-term goal, promising diverse and novel applications. Yet, reinforcement
learning has only achieved limited impact on real-time robot control due to its
high demand of real-world interactions. In this work, by leveraging a learnt
probabilistic model of drone dynamics, we learn a thrust-attitude controller
for a quadrotor through model-based reinforcement learning. No prior knowledge
of the flight dynamics is assumed; instead, a sequential latent variable model,
used generatively and as an online filter, is learnt from raw sensory input.
The controller and value function are optimised entirely by propagating
stochastic analytic gradients through generated latent trajectories. We show
that "learning to fly" can be achieved with less than 30 minutes of experience
with a single drone, and can be deployed solely using onboard computational
resources and sensors, on a self-built drone.

13
The categories of this paper are: quant-ph
32
The categories of this paper are: atom-ph
29
The categories of this paper are: funct-an
27
The categories of this paper are: comp-gas
36
The categories of this paper are: cmp-lg
18
The categories of this paper are: econ
8
The categories of this paper are: hep-ex
11
The categories of this paper are: hep-lat
24
The categories of this paper are: dg-ga
14
The categories of this paper are: math-ph
22
The categories of this paper are: chao-dyn
20
The categories of this paper are: acc-phys
26
The categories of this paper are: bayes-an
25
The categories of this paper are: patt-sol
7
The categories of this paper are: hep-th
37
The categories of this paper are: supr-con