Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning Sparse Representations in Reinforcement Learning with Sparse
  Coding   A variety of representation learning approaches have been investigated for
reinforcement learning; much less attention, however, has been given to
investigating the utility of sparse coding. Outside of reinforcement learning,
sparse coding representations have been widely used, with non-convex objectives
that result in discriminative representations. In this work, we develop a
supervised sparse coding objective for policy evaluation. Despite the
non-convexity of this objective, we prove that all local minima are global
minima, making the approach amenable to simple optimization strategies. We
empirically show that it is key to use a supervised objective, rather than the
more straightforward unsupervised sparse coding approach. We compare the
learned representations to a canonical fixed sparse representation, called
tile-coding, demonstrating that the sparse coding representation outperforms a
wide variety of tilecoding representations.

36
The categories of this paper are: cmp-lg
23
The categories of this paper are: solv-int
11
The categories of this paper are: hep-lat
2
The categories of this paper are: math
10
The categories of this paper are: q-bio
14
The categories of this paper are: math-ph
22
The categories of this paper are: chao-dyn
32
The categories of this paper are: atom-ph
7
The categories of this paper are: hep-th
0
The categories of this paper are: hep-ph
37
The categories of this paper are: supr-con
34
The categories of this paper are: chem-ph
8
The categories of this paper are: hep-ex
6
The categories of this paper are: astro-ph
26
The categories of this paper are: bayes-an
35
The categories of this paper are: mtrl-th