Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Unified View of TD Algorithms; Introducing Full-Gradient TD and
  Equi-Gradient Descent TD   This paper addresses the issue of policy evaluation in Markov Decision
Processes, using linear function approximation. It provides a unified view of
algorithms such as TD(lambda), LSTD(lambda), iLSTD, residual-gradient TD. It is
asserted that they all consist in minimizing a gradient function and differ by
the form of this function and their means of minimizing it. Two new schemes are
introduced in that framework: Full-gradient TD which uses a generalization of
the principle introduced in iLSTD, and EGD TD, which reduces the gradient by
successive equi-gradient descents. These three algorithms form a new
intermediate family with the interesting property of making much better use of
the samples than TD while keeping a gradient descent scheme, which is useful
for complexity issues and optimistic policy iteration.

12
The categories of this paper are: nucl-th
4
The categories of this paper are: cond-mat
25
The categories of this paper are: patt-sol
7
The categories of this paper are: hep-th
31
The categories of this paper are: ao-sci
27
The categories of this paper are: comp-gas
6
The categories of this paper are: astro-ph
32
The categories of this paper are: atom-ph
24
The categories of this paper are: dg-ga
35
The categories of this paper are: mtrl-th
17
The categories of this paper are: q-fin
9
The categories of this paper are: nlin
8
The categories of this paper are: hep-ex
21
The categories of this paper are: adap-org
23
The categories of this paper are: solv-int
14
The categories of this paper are: math-ph