Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Ranking Policy Gradient   Sample inefficiency is a long-lasting problem in reinforcement learning (RL).
The state-of-the-art estimates the optimal action values while it usually
involves an extensive search over the state-action space and unstable
optimization. Towards the sample-efficient RL, we propose ranking policy
gradient (RPG), a policy gradient method that learns the optimal rank of a set
of discrete actions. To accelerate the learning of policy gradient methods, we
establish the equivalence between maximizing the lower bound of return and
imitating a near-optimal policy without accessing any oracles. These results
lead to a general off-policy learning framework, which preserves the
optimality, reduces variance, and improves the sample-efficiency. Furthermore,
the sample complexity of RPG does not depend on the dimension of state space,
which enables RPG for large-scale problems. We conduct extensive experiments
showing that when consolidating with the off-policy learning framework, RPG
substantially reduces the sample complexity, comparing to the state-of-the-art.

34
The categories of this paper are: chem-ph
4
The categories of this paper are: cond-mat
17
The categories of this paper are: q-fin
22
The categories of this paper are: chao-dyn
12
The categories of this paper are: nucl-th
24
The categories of this paper are: dg-ga
8
The categories of this paper are: hep-ex
35
The categories of this paper are: mtrl-th
28
The categories of this paper are: alg-geom
13
The categories of this paper are: quant-ph
0
The categories of this paper are: hep-ph
37
The categories of this paper are: supr-con
18
The categories of this paper are: econ
7
The categories of this paper are: hep-th
33
The categories of this paper are: plasm-ph
25
The categories of this paper are: patt-sol