Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Convergence of Multi-Agent Learning with a Finite Step Size in
  General-Sum Games   Learning in a multi-agent system is challenging because agents are
simultaneously learning and the environment is not stationary, undermining
convergence guarantees. To address this challenge, this paper presents a new
gradient-based learning algorithm, called Gradient Ascent with Shrinking Policy
Prediction (GA-SPP), which augments the basic gradient ascent approach with the
concept of shrinking policy prediction. The key idea behind this algorithm is
that an agent adjusts its strategy in response to the forecasted strategy of
the other agent, instead of its current one. GA-SPP is shown formally to have
Nash convergence in larger settings than existing gradient-based multi-agent
learning methods. Furthermore, unlike existing gradient-based methods, GA-SPP's
theoretical guarantees do not assume the learning rate to be infinitesimal.

21
The categories of this paper are: adap-org
9
The categories of this paper are: nlin
12
The categories of this paper are: nucl-th
6
The categories of this paper are: astro-ph
31
The categories of this paper are: ao-sci
23
The categories of this paper are: solv-int
22
The categories of this paper are: chao-dyn
3
The categories of this paper are: physics
2
The categories of this paper are: math
34
The categories of this paper are: chem-ph
37
The categories of this paper are: supr-con
27
The categories of this paper are: comp-gas
28
The categories of this paper are: alg-geom
30
The categories of this paper are: q-alg
4
The categories of this paper are: cond-mat
7
The categories of this paper are: hep-th