2,631 research outputs found
Scale-free memory model for multiagent reinforcement learning. Mean field approximation and rock-paper-scissors dynamics
A continuous time model for multiagent systems governed by reinforcement
learning with scale-free memory is developed. The agents are assumed to act
independently of one another in optimizing their choice of possible actions via
trial-and-error search. To gain awareness about the action value the agents
accumulate in their memory the rewards obtained from taking a specific action
at each moment of time. The contribution of the rewards in the past to the
agent current perception of action value is described by an integral operator
with a power-law kernel. Finally a fractional differential equation governing
the system dynamics is obtained. The agents are considered to interact with one
another implicitly via the reward of one agent depending on the choice of the
other agents. The pairwise interaction model is adopted to describe this
effect. As a specific example of systems with non-transitive interactions, a
two agent and three agent systems of the rock-paper-scissors type are analyzed
in detail, including the stability analysis and numerical simulation.
Scale-free memory is demonstrated to cause complex dynamics of the systems at
hand. In particular, it is shown that there can be simultaneously two modes of
the system instability undergoing subcritical and supercritical bifurcation,
with the latter one exhibiting anomalous oscillations with the amplitude and
period growing with time. Besides, the instability onset via this supercritical
mode may be regarded as "altruism self-organization". For the three agent
system the instability dynamics is found to be rather irregular and can be
composed of alternate fragments of oscillations different in their properties.Comment: 17 pages, 7 figur
Robust Mission Design Through Evidence Theory and Multi-Agent Collaborative Search
In this paper, the preliminary design of a space mission is approached
introducing uncertainties on the design parameters and formulating the
resulting reliable design problem as a multiobjective optimization problem.
Uncertainties are modelled through evidence theory and the belief, or
credibility, in the successful achievement of mission goals is maximised along
with the reliability of constraint satisfaction. The multiobjective
optimisation problem is solved through a novel algorithm based on the
collaboration of a population of agents in search for the set of highly
reliable solutions. Two typical problems in mission analysis are used to
illustrate the proposed methodology
Multi-agent systems for power engineering applications - part 1 : Concepts, approaches and technical challenges
This is the first part of a 2-part paper that has arisen from the work of the IEEE Power Engineering Society's Multi-Agent Systems (MAS) Working Group. Part 1 of the paper examines the potential value of MAS technology to the power industry. In terms of contribution, it describes fundamental concepts and approaches within the field of multi-agent systems that are appropriate to power engineering applications. As well as presenting a comprehensive review of the meaningful power engineering applications for which MAS are being investigated, it also defines the technical issues which must be addressed in order to accelerate and facilitate the uptake of the technology within the power and energy sector. Part 2 of the paper explores the decisions inherent in engineering multi-agent systems for applications in the power and energy sector and offers guidance and recommendations on how MAS can be designed and implemented
Sparse Stabilization and Control of Alignment Models
From a mathematical point of view self-organization can be described as
patterns to which certain dynamical systems modeling social dynamics tend
spontaneously to be attracted. In this paper we explore situations beyond
self-organization, in particular how to externally control such dynamical
systems in order to eventually enforce pattern formation also in those
situations where this wished phenomenon does not result from spontaneous
convergence. Our focus is on dynamical systems of Cucker-Smale type, modeling
consensus emergence, and we question the existence of stabilization and optimal
control strategies which require the minimal amount of external intervention
for nevertheless inducing consensus in a group of interacting agents. We
provide a variational criterion to explicitly design feedback controls that are
componentwise sparse, i.e. with at most one nonzero component at every instant
of time. Controls sharing this sparsity feature are very realistic and
convenient for practical issues. Moreover, the maximally sparse ones are
instantaneously optimal in terms of the decay rate of a suitably designed
Lyapunov functional, measuring the distance from consensus. As a consequence we
provide a mathematical justification to the general principle according to
which "sparse is better" in the sense that a policy maker, who is not allowed
to predict future developments, should always consider more favorable to
intervene with stronger action on the fewest possible instantaneous optimal
leaders rather than trying to control more agents with minor strength in order
to achieve group consensus. We then establish local and global sparse
controllability properties to consensus and, finally, we analyze the sparsity
of solutions of the finite time optimal control problem where the minimization
criterion is a combination of the distance from consensus and of the l1-norm of
the control.Comment: 33 pages, 5 figure
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