Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Overcoming Exploration: Deep Reinforcement Learning in Complex
  Environments from Temporal Logic Specifications   We present a Deep Reinforcement Learning (DRL) algorithm for a task-guided
robot with unknown continuous-time dynamics deployed in a large-scale complex
environment. Linear Temporal Logic (LTL) is applied to express a rich robotic
specification. To overcome the environmental challenge, we propose a novel path
planning-guided reward scheme that is dense over the state space, and
crucially, robust to infeasibility of computed geometric paths due to the
unknown robot dynamics. To facilitate LTL satisfaction, our approach decomposes
the LTL mission into sub-tasks that are solved using distributed DRL, where the
sub-tasks are trained in parallel, using Deep Policy Gradient algorithms. Our
framework is shown to significantly improve performance (effectiveness,
efficiency) and exploration of robots tasked with complex missions in
large-scale complex environments.

16
The categories of this paper are: stat
18
The categories of this paper are: econ
32
The categories of this paper are: atom-ph
12
The categories of this paper are: nucl-th
9
The categories of this paper are: nlin
31
The categories of this paper are: ao-sci
33
The categories of this paper are: plasm-ph
25
The categories of this paper are: patt-sol
29
The categories of this paper are: funct-an
2
The categories of this paper are: math
19
The categories of this paper are: eess
5
The categories of this paper are: gr-qc
7
The categories of this paper are: hep-th
36
The categories of this paper are: cmp-lg
17
The categories of this paper are: q-fin
28
The categories of this paper are: alg-geom