476,869 research outputs found
Multi-agent decision-making dynamics inspired by honeybees
When choosing between candidate nest sites, a honeybee swarm reliably chooses
the most valuable site and even when faced with the choice between near-equal
value sites, it makes highly efficient decisions. Value-sensitive
decision-making is enabled by a distributed social effort among the honeybees,
and it leads to decision-making dynamics of the swarm that are remarkably
robust to perturbation and adaptive to change. To explore and generalize these
features to other networks, we design distributed multi-agent network dynamics
that exhibit a pitchfork bifurcation, ubiquitous in biological models of
decision-making. Using tools of nonlinear dynamics we show how the designed
agent-based dynamics recover the high performing value-sensitive
decision-making of the honeybees and rigorously connect investigation of
mechanisms of animal group decision-making to systematic, bio-inspired control
of multi-agent network systems. We further present a distributed adaptive
bifurcation control law and prove how it enhances the network decision-making
performance beyond that observed in swarms
Towards time-varying proximal dynamics in Multi-Agent Network Games
Distributed decision making in multi-agent networks has recently attracted
significant research attention thanks to its wide applicability, e.g. in the
management and optimization of computer networks, power systems, robotic teams,
sensor networks and consumer markets. Distributed decision-making problems can
be modeled as inter-dependent optimization problems, i.e., multi-agent
game-equilibrium seeking problems, where noncooperative agents seek an
equilibrium by communicating over a network. To achieve a network equilibrium,
the agents may decide to update their decision variables via proximal dynamics,
driven by the decision variables of the neighboring agents. In this paper, we
provide an operator-theoretic characterization of convergence with a
time-invariant communication network. For the time-varying case, we consider
adjacency matrices that may switch subject to a dwell time. We illustrate our
investigations using a distributed robotic exploration example.Comment: 6 pages, 3 figure
A Reduction-based Framework for Sequential Decision Making with Delayed Feedback
We study stochastic delayed feedback in general multi-agent sequential
decision making, which includes bandits, single-agent Markov decision processes
(MDPs), and Markov games (MGs). We propose a novel reduction-based framework,
which turns any multi-batched algorithm for sequential decision making with
instantaneous feedback into a sample-efficient algorithm that can handle
stochastic delays in sequential decision making. By plugging different
multi-batched algorithms into our framework, we provide several examples
demonstrating that our framework not only matches or improves existing results
for bandits, tabular MDPs, and tabular MGs, but also provides the first line of
studies on delays in sequential decision making with function approximation. In
summary, we provide a complete set of sharp results for multi-agent sequential
decision making with delayed feedback.Comment: Accepted by Neurips 2023. arXiv admin note: text overlap with
arXiv:2110.14555 by other author
Fairness in Multi-Agent Sequential Decision-Making
We define a fairness solution criterion for multi-agent decision-making problems, where agents have local interests. This new criterion aims to maximize the worst performance of agents with consideration on the overall performance. We develop a simple linear programming approach and a more scalable game-theoretic approach for computing an optimal fairness policy. This game-theoretic approach formulates this fairness optimization as a two-player, zero-sum game and employs an iterative algorithm for finding a Nash equilibrium, corresponding to an optimal fairness policy. We scale up this approach by exploiting problem structure and value function approximation. Our experiments on resource allocation problems show that this fairness criterion provides a more favorable solution than the utilitarian criterion, and that our game-theoretic approach is significantly faster than linear programming
Masked Pretraining for Multi-Agent Decision Making
Building a single generalist agent with zero-shot capability has recently
sparked significant advancements in decision-making. However, extending this
capability to multi-agent scenarios presents challenges. Most current works
struggle with zero-shot capabilities, due to two challenges particular to the
multi-agent settings: a mismatch between centralized pretraining and
decentralized execution, and varying agent numbers and action spaces, making it
difficult to create generalizable representations across diverse downstream
tasks. To overcome these challenges, we propose a \textbf{Mask}ed pretraining
framework for \textbf{M}ulti-\textbf{a}gent decision making (MaskMA). This
model, based on transformer architecture, employs a mask-based collaborative
learning strategy suited for decentralized execution with partial observation.
Moreover, MaskMA integrates a generalizable action representation by dividing
the action space into actions toward self-information and actions related to
other entities. This flexibility allows MaskMA to tackle tasks with varying
agent numbers and thus different action spaces. Extensive experiments in SMAC
reveal MaskMA, with a single model pretrained on 11 training maps, can achieve
an impressive 77.8% zero-shot win rate on 60 unseen test maps by decentralized
execution, while also performing effectively on other types of downstream tasks
(\textit{e.g.,} varied policies collaboration and ad hoc team play).Comment: 17 page
Human Decision-Making in Multi-Agent Systems
In order to avoid suboptimal collective behaviors and resolve social dilemmas, researchers have tried to understand how humans make decisions when interacting with other humans or smart machines and carried out theoretical and experimental studies aimed at influencing decision-making dynamics in large populations. We identify the key challenges and open issues in the related research, list a few popular models with the corresponding results, and point out future research directions
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