Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Continual State Representation Learning for Reinforcement Learning using
  Generative Replay   We consider the problem of building a state representation model in a
continual fashion. As the environment changes, the aim is to efficiently
compress the sensory state's information without losing past knowledge. The
learned features are then fed to a Reinforcement Learning algorithm to learn a
policy. We propose to use Variational Auto-Encoders for state representation,
and Generative Replay, i.e. the use of generated samples, to maintain past
knowledge. We also provide a general and statistically sound method for
automatic environment change detection. Our method provides efficient state
representation as well as forward transfer, and avoids catastrophic forgetting.
The resulting model is capable of incrementally learning information without
using past data and with a bounded system size.

36
The categories of this paper are: cmp-lg
33
The categories of this paper are: plasm-ph
32
The categories of this paper are: atom-ph
18
The categories of this paper are: econ
14
The categories of this paper are: math-ph
37
The categories of this paper are: supr-con
20
The categories of this paper are: acc-phys
28
The categories of this paper are: alg-geom
22
The categories of this paper are: chao-dyn
0
The categories of this paper are: hep-ph
2
The categories of this paper are: math
11
The categories of this paper are: hep-lat
17
The categories of this paper are: q-fin
25
The categories of this paper are: patt-sol
19
The categories of this paper are: eess
26
The categories of this paper are: bayes-an