Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Data-Efficient Reinforcement Learning with Probabilistic Model
  Predictive Control   Trial-and-error based reinforcement learning (RL) has seen rapid advancements
in recent times, especially with the advent of deep neural networks. However,
the majority of autonomous RL algorithms require a large number of interactions
with the environment. A large number of interactions may be impractical in many
real-world applications, such as robotics, and many practical systems have to
obey limitations in the form of state space or control constraints. To reduce
the number of system interactions while simultaneously handling constraints, we
propose a model-based RL framework based on probabilistic Model Predictive
Control (MPC). In particular, we propose to learn a probabilistic transition
model using Gaussian Processes (GPs) to incorporate model uncertainty into
long-term predictions, thereby, reducing the impact of model errors. We then
use MPC to find a control sequence that minimises the expected long-term cost.
We provide theoretical guarantees for first-order optimality in the GP-based
transition models with deterministic approximate inference for long-term
planning. We demonstrate that our approach does not only achieve
state-of-the-art data efficiency, but also is a principled way for RL in
constrained environments.

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The categories of this paper are: atom-ph
22
The categories of this paper are: chao-dyn
14
The categories of this paper are: math-ph
12
The categories of this paper are: nucl-th
20
The categories of this paper are: acc-phys
33
The categories of this paper are: plasm-ph
2
The categories of this paper are: math
30
The categories of this paper are: q-alg
15
The categories of this paper are: nucl-ex
7
The categories of this paper are: hep-th
19
The categories of this paper are: eess
37
The categories of this paper are: supr-con
27
The categories of this paper are: comp-gas
18
The categories of this paper are: econ
8
The categories of this paper are: hep-ex
36
The categories of this paper are: cmp-lg