Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Attraction-Repulsion Actor-Critic for Continuous Control Reinforcement
  Learning   Continuous control tasks in reinforcement learning are important because they
provide an important framework for learning in high-dimensional state spaces
with deceptive rewards, where the agent can easily become trapped into
suboptimal solutions. One way to avoid local optima is to use a population of
agents to ensure coverage of the policy space, yet learning a population with
the "best" coverage is still an open problem. In this work, we present a novel
approach to population-based RL in continuous control that leverages properties
of normalizing flows to perform attractive and repulsive operations between
current members of the population and previously observed policies. Empirical
results on the MuJoCo suite demonstrate a high performance gain for our
algorithm compared to prior work, including Soft-Actor Critic (SAC).

31
The categories of this paper are: ao-sci
22
The categories of this paper are: chao-dyn
36
The categories of this paper are: cmp-lg
28
The categories of this paper are: alg-geom
13
The categories of this paper are: quant-ph
8
The categories of this paper are: hep-ex
35
The categories of this paper are: mtrl-th
20
The categories of this paper are: acc-phys
19
The categories of this paper are: eess
10
The categories of this paper are: q-bio
12
The categories of this paper are: nucl-th
3
The categories of this paper are: physics
17
The categories of this paper are: q-fin
0
The categories of this paper are: hep-ph
26
The categories of this paper are: bayes-an
33
The categories of this paper are: plasm-ph