Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Disentangled Skill Embeddings for Reinforcement Learning   We propose a novel framework for multi-task reinforcement learning (MTRL).
Using a variational inference formulation, we learn policies that generalize
across both changing dynamics and goals. The resulting policies are
parametrized by shared parameters that allow for transfer between different
dynamics and goal conditions, and by task-specific latent-space embeddings that
allow for specialization to particular tasks. We show how the latent-spaces
enable generalization to unseen dynamics and goals conditions. Additionally,
policies equipped with such embeddings serve as a space of skills (or options)
for hierarchical reinforcement learning. Since we can change task dynamics and
goals independently, we name our framework Disentangled Skill Embeddings (DSE).

23
The categories of this paper are: solv-int
6
The categories of this paper are: astro-ph
22
The categories of this paper are: chao-dyn
17
The categories of this paper are: q-fin
25
The categories of this paper are: patt-sol
3
The categories of this paper are: physics
20
The categories of this paper are: acc-phys
28
The categories of this paper are: alg-geom
14
The categories of this paper are: math-ph
2
The categories of this paper are: math
35
The categories of this paper are: mtrl-th
26
The categories of this paper are: bayes-an
5
The categories of this paper are: gr-qc
30
The categories of this paper are: q-alg
0
The categories of this paper are: hep-ph
29
The categories of this paper are: funct-an