Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Model Free Reinforcement Learning Algorithm for Stationary Mean field
  Equilibrium for Multiple Types of Agents   We consider a multi-agent Markov strategic interaction over an infinite
horizon where agents can be of multiple types. We model the strategic
interaction as a mean-field game in the asymptotic limit when the number of
agents of each type becomes infinite. Each agent has a private state; the state
evolves depending on the distribution of the state of the agents of different
types and the action of the agent. Each agent wants to maximize the discounted
sum of rewards over the infinite horizon which depends on the state of the
agent and the distribution of the state of the leaders and followers. We seek
to characterize and compute a stationary multi-type Mean field equilibrium
(MMFE) in the above game. We characterize the conditions under which a
stationary MMFE exists. Finally, we propose Reinforcement learning (RL) based
algorithm using policy gradient approach to find the stationary MMFE when the
agents are unaware of the dynamics. We, numerically, evaluate how such kind of
interaction can model the cyber attacks among defenders and adversaries, and
show how RL based algorithm can converge to an equilibrium.

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The categories of this paper are: cond-mat
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The categories of this paper are: astro-ph
18
The categories of this paper are: econ
25
The categories of this paper are: patt-sol
17
The categories of this paper are: q-fin
31
The categories of this paper are: ao-sci
21
The categories of this paper are: adap-org
7
The categories of this paper are: hep-th
36
The categories of this paper are: cmp-lg
20
The categories of this paper are: acc-phys
26
The categories of this paper are: bayes-an
15
The categories of this paper are: nucl-ex
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The categories of this paper are: quant-ph
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The categories of this paper are: q-bio
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The categories of this paper are: stat
22
The categories of this paper are: chao-dyn