Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Reinforcement Learning for Mean Field Games with Strategic
  Complementarities   Mean Field Games (MFG) are the class of games with a very large number of
agents and the standard equilibrium concept is a Mean Field Equilibrium (MFE).
Algorithms for learning MFE in dynamic MFGs are unknown in general. Our focus
is on an important subclass that possess a monotonicity property called
Strategic Complementarities (MFG-SC). We introduce a natural refinement to the
equilibrium concept that we call Trembling-Hand-Perfect MFE (T-MFE), which
allows agents to employ a measure of randomization while accounting for the
impact of such randomization on their payoffs. We propose a simple algorithm
for computing T-MFE under a known model. We also introduce a model-free and a
model-based approach to learning T-MFE and provide sample complexities of both
algorithms. We also develop a fully online learning scheme that obviates the
need for a simulator. Finally, we empirically evaluate the performance of the
proposed algorithms via examples motivated by real-world applications.

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The categories of this paper are: hep-ph
18
The categories of this paper are: econ
28
The categories of this paper are: alg-geom
24
The categories of this paper are: dg-ga
3
The categories of this paper are: physics
10
The categories of this paper are: q-bio
8
The categories of this paper are: hep-ex
33
The categories of this paper are: plasm-ph
5
The categories of this paper are: gr-qc
13
The categories of this paper are: quant-ph
4
The categories of this paper are: cond-mat
23
The categories of this paper are: solv-int
35
The categories of this paper are: mtrl-th
27
The categories of this paper are: comp-gas
25
The categories of this paper are: patt-sol
22
The categories of this paper are: chao-dyn