Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Multi-Agent Distributed Reinforcement Learning for Making Decentralized
  Offloading Decisions   We formulate computation offloading as a decentralized decision-making
problem with autonomous agents. We design an interaction mechanism that
incentivizes agents to align private and system goals by balancing between
competition and cooperation. The mechanism provably has Nash equilibria with
optimal resource allocation in the static case. For a dynamic environment, we
propose a novel multi-agent online learning algorithm that learns with partial,
delayed and noisy state information, and a reward signal that reduces
information need to a great extent. Empirical results confirm that through
learning, agents significantly improve both system and individual performance,
e.g., 40% offloading failure rate reduction, 32% communication overhead
reduction, up to 38% computation resource savings in low contention, 18%
utilization increase with reduced load variation in high contention, and
improvement in fairness. Results also confirm the algorithm's good convergence
and generalization property in significantly different environments.

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The categories of this paper are: astro-ph
17
The categories of this paper are: q-fin
31
The categories of this paper are: ao-sci
10
The categories of this paper are: q-bio
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The categories of this paper are: hep-ex
13
The categories of this paper are: quant-ph
23
The categories of this paper are: solv-int
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The categories of this paper are: dg-ga
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The categories of this paper are: math
27
The categories of this paper are: comp-gas
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The categories of this paper are: cmp-lg
16
The categories of this paper are: stat
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The categories of this paper are: hep-lat
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The categories of this paper are: gr-qc
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The categories of this paper are: acc-phys
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The categories of this paper are: eess