Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Grounding Hindsight Instructions in Multi-Goal Reinforcement Learning
  for Robotics   This paper focuses on robotic reinforcement learning with sparse rewards for
natural language goal representations. An open problem is the
sample-inefficiency that stems from the compositionality of natural language,
and from the grounding of language in sensory data and actions. We address
these issues with three contributions. We first present a mechanism for
hindsight instruction replay utilizing expert feedback. Second, we propose a
seq2seq model to generate linguistic hindsight instructions. Finally, we
present a novel class of language-focused learning tasks. We show that
hindsight instructions improve the learning performance, as expected. In
addition, we also provide an unexpected result: We show that the learning
performance of our agent can be improved by one third if, in a sense, the agent
learns to talk to itself in a self-supervised manner. We achieve this by
learning to generate linguistic instructions that would have been appropriate
as a natural language goal for an originally unintended behavior. Our results
indicate that the performance gain increases with the task-complexity.

11
The categories of this paper are: hep-lat
22
The categories of this paper are: chao-dyn
34
The categories of this paper are: chem-ph
30
The categories of this paper are: q-alg
23
The categories of this paper are: solv-int
15
The categories of this paper are: nucl-ex
12
The categories of this paper are: nucl-th
26
The categories of this paper are: bayes-an
32
The categories of this paper are: atom-ph
19
The categories of this paper are: eess
3
The categories of this paper are: physics
7
The categories of this paper are: hep-th
35
The categories of this paper are: mtrl-th
9
The categories of this paper are: nlin
28
The categories of this paper are: alg-geom
31
The categories of this paper are: ao-sci