Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: The Divergence of Reinforcement Learning Algorithms with Value-Iteration
  and Function Approximation   This paper gives specific divergence examples of value-iteration for several
major Reinforcement Learning and Adaptive Dynamic Programming algorithms, when
using a function approximator for the value function. These divergence examples
differ from previous divergence examples in the literature, in that they are
applicable for a greedy policy, i.e. in a "value iteration" scenario. Perhaps
surprisingly, with a greedy policy, it is also possible to get divergence for
the algorithms TD(1) and Sarsa(1). In addition to these divergences, we also
achieve divergence for the Adaptive Dynamic Programming algorithms HDP, DHP and
GDHP.

13
The categories of this paper are: quant-ph
15
The categories of this paper are: nucl-ex
24
The categories of this paper are: dg-ga
27
The categories of this paper are: comp-gas
17
The categories of this paper are: q-fin
21
The categories of this paper are: adap-org
35
The categories of this paper are: mtrl-th
5
The categories of this paper are: gr-qc
11
The categories of this paper are: hep-lat
31
The categories of this paper are: ao-sci
7
The categories of this paper are: hep-th
2
The categories of this paper are: math
10
The categories of this paper are: q-bio
23
The categories of this paper are: solv-int
33
The categories of this paper are: plasm-ph
32
The categories of this paper are: atom-ph