Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Hierarchy through Composition with Linearly Solvable Markov Decision
  Processes   Hierarchical architectures are critical to the scalability of reinforcement
learning methods. Current hierarchical frameworks execute actions serially,
with macro-actions comprising sequences of primitive actions. We propose a
novel alternative to these control hierarchies based on concurrent execution of
many actions in parallel. Our scheme uses the concurrent compositionality
provided by the linearly solvable Markov decision process (LMDP) framework,
which naturally enables a learning agent to draw on several macro-actions
simultaneously to solve new tasks. We introduce the Multitask LMDP module,
which maintains a parallel distributed representation of tasks and may be
stacked to form deep hierarchies abstracted in space and time.

15
The categories of this paper are: nucl-ex
18
The categories of this paper are: econ
13
The categories of this paper are: quant-ph
28
The categories of this paper are: alg-geom
34
The categories of this paper are: chem-ph
25
The categories of this paper are: patt-sol
32
The categories of this paper are: atom-ph
35
The categories of this paper are: mtrl-th
7
The categories of this paper are: hep-th
12
The categories of this paper are: nucl-th
10
The categories of this paper are: q-bio
0
The categories of this paper are: hep-ph
8
The categories of this paper are: hep-ex
27
The categories of this paper are: comp-gas
37
The categories of this paper are: supr-con
11
The categories of this paper are: hep-lat