Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Improving Generalization in Meta Reinforcement Learning using Learned
  Objectives   Biological evolution has distilled the experiences of many learners into the
general learning algorithms of humans. Our novel meta reinforcement learning
algorithm MetaGenRL is inspired by this process. MetaGenRL distills the
experiences of many complex agents to meta-learn a low-complexity neural
objective function that decides how future individuals will learn. Unlike
recent meta-RL algorithms, MetaGenRL can generalize to new environments that
are entirely different from those used for meta-training. In some cases, it
even outperforms human-engineered RL algorithms. MetaGenRL uses off-policy
second-order gradients during meta-training that greatly increase its sample
efficiency.

9
The categories of this paper are: nlin
5
The categories of this paper are: gr-qc
24
The categories of this paper are: dg-ga
30
The categories of this paper are: q-alg
35
The categories of this paper are: mtrl-th
19
The categories of this paper are: eess
27
The categories of this paper are: comp-gas
0
The categories of this paper are: hep-ph
18
The categories of this paper are: econ
4
The categories of this paper are: cond-mat
14
The categories of this paper are: math-ph
10
The categories of this paper are: q-bio
28
The categories of this paper are: alg-geom
29
The categories of this paper are: funct-an
15
The categories of this paper are: nucl-ex
13
The categories of this paper are: quant-ph