Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Importance Weighted Policy Learning and Adaptation   The ability to exploit prior experience to solve novel problems rapidly is a
hallmark of biological learning systems and of great practical importance for
artificial ones. In the meta reinforcement learning literature much recent work
has focused on the problem of optimizing the learning process itself. In this
paper we study a complementary approach which is conceptually simple, general,
modular and built on top of recent improvements in off-policy learning. The
framework is inspired by ideas from the probabilistic inference literature and
combines robust off-policy learning with a behavior prior, or default behavior
that constrains the space of solutions and serves as a bias for exploration; as
well as a representation for the value function, both of which are easily
learned from a number of training tasks in a multi-task scenario. Our approach
achieves competitive adaptation performance on hold-out tasks compared to meta
reinforcement learning baselines and can scale to complex sparse-reward
scenarios.

34
The categories of this paper are: chem-ph
23
The categories of this paper are: solv-int
20
The categories of this paper are: acc-phys
10
The categories of this paper are: q-bio
0
The categories of this paper are: hep-ph
15
The categories of this paper are: nucl-ex
21
The categories of this paper are: adap-org
35
The categories of this paper are: mtrl-th
12
The categories of this paper are: nucl-th
27
The categories of this paper are: comp-gas
8
The categories of this paper are: hep-ex
19
The categories of this paper are: eess
26
The categories of this paper are: bayes-an
32
The categories of this paper are: atom-ph
33
The categories of this paper are: plasm-ph
5
The categories of this paper are: gr-qc