Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Dot-to-Dot: Explainable Hierarchical Reinforcement Learning for Robotic
  Manipulation   Robotic systems are ever more capable of automation and fulfilment of complex
tasks, particularly with reliance on recent advances in intelligent systems,
deep learning and artificial intelligence. However, as robots and humans come
closer in their interactions, the matter of interpretability, or explainability
of robot decision-making processes for the human grows in importance. A
successful interaction and collaboration will only take place through mutual
understanding of underlying representations of the environment and the task at
hand. This is currently a challenge in deep learning systems. We present a
hierarchical deep reinforcement learning system, consisting of a low-level
agent handling the large actions/states space of a robotic system efficiently,
by following the directives of a high-level agent which is learning the
high-level dynamics of the environment and task. This high-level agent forms a
representation of the world and task at hand that is interpretable for a human
operator. The method, which we call Dot-to-Dot, is tested on a MuJoCo-based
model of the Fetch Robotics Manipulator, as well as a Shadow Hand, to test its
performance. Results show efficient learning of complex actions/states spaces
by the low-level agent, and an interpretable representation of the task and
decision-making process learned by the high-level agent.

12
The categories of this paper are: nucl-th
13
The categories of this paper are: quant-ph
6
The categories of this paper are: astro-ph
8
The categories of this paper are: hep-ex
15
The categories of this paper are: nucl-ex
32
The categories of this paper are: atom-ph
3
The categories of this paper are: physics
36
The categories of this paper are: cmp-lg
19
The categories of this paper are: eess
11
The categories of this paper are: hep-lat
22
The categories of this paper are: chao-dyn
2
The categories of this paper are: math
35
The categories of this paper are: mtrl-th
34
The categories of this paper are: chem-ph
26
The categories of this paper are: bayes-an
5
The categories of this paper are: gr-qc