Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Unsupervised Learning and Exploration of Reachable Outcome Space   Performing Reinforcement Learning in sparse rewards settings, with very
little prior knowledge, is a challenging problem since there is no signal to
properly guide the learning process. In such situations, a good search strategy
is fundamental. At the same time, not having to adapt the algorithm to every
single problem is very desirable. Here we introduce TAXONS, a Task Agnostic
eXploration of Outcome spaces through Novelty and Surprise algorithm. Based on
a population-based divergent-search approach, it learns a set of diverse
policies directly from high-dimensional observations, without any task-specific
information. TAXONS builds a repertoire of policies while training an
autoencoder on the high-dimensional observation of the final state of the
system to build a low-dimensional outcome space. The learned outcome space,
combined with the reconstruction error, is used to drive the search for new
policies. Results show that TAXONS can find a diverse set of controllers,
covering a good part of the ground-truth outcome space, while having no
information about such space.

23
The categories of this paper are: solv-int
24
The categories of this paper are: dg-ga
32
The categories of this paper are: atom-ph
22
The categories of this paper are: chao-dyn
34
The categories of this paper are: chem-ph
9
The categories of this paper are: nlin
14
The categories of this paper are: math-ph
27
The categories of this paper are: comp-gas
20
The categories of this paper are: acc-phys
37
The categories of this paper are: supr-con
30
The categories of this paper are: q-alg
13
The categories of this paper are: quant-ph
4
The categories of this paper are: cond-mat
19
The categories of this paper are: eess
3
The categories of this paper are: physics
6
The categories of this paper are: astro-ph