Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning Routines for Effective Off-Policy Reinforcement Learning   The performance of reinforcement learning depends upon designing an
appropriate action space, where the effect of each action is measurable, yet,
granular enough to permit flexible behavior. So far, this process involved
non-trivial user choices in terms of the available actions and their execution
frequency. We propose a novel framework for reinforcement learning that
effectively lifts such constraints. Within our framework, agents learn
effective behavior over a routine space: a new, higher-level action space,
where each routine represents a set of 'equivalent' sequences of granular
actions with arbitrary length. Our routine space is learned end-to-end to
facilitate the accomplishment of underlying off-policy reinforcement learning
objectives. We apply our framework to two state-of-the-art off-policy
algorithms and show that the resulting agents obtain relevant performance
improvements while requiring fewer interactions with the environment per
episode, improving computational efficiency.

3
The categories of this paper are: physics
36
The categories of this paper are: cmp-lg
33
The categories of this paper are: plasm-ph
14
The categories of this paper are: math-ph
5
The categories of this paper are: gr-qc
6
The categories of this paper are: astro-ph
31
The categories of this paper are: ao-sci
0
The categories of this paper are: hep-ph
16
The categories of this paper are: stat
32
The categories of this paper are: atom-ph
24
The categories of this paper are: dg-ga
26
The categories of this paper are: bayes-an
11
The categories of this paper are: hep-lat
22
The categories of this paper are: chao-dyn
19
The categories of this paper are: eess
18
The categories of this paper are: econ