15,231 research outputs found
Steering the distribution of agents in mean-field and cooperative games
The purpose of this work is to pose and solve the problem to guide a
collection of weakly interacting dynamical systems (agents, particles, etc.) to
a specified terminal distribution. The framework is that of mean-field and of
cooperative games. A terminal cost is used to accomplish the task; we establish
that the map between terminal costs and terminal probability distributions is
onto. Our approach relies on and extends the theory of optimal mass transport
and its generalizations.Comment: 20 pages, 8 figure
Steering the Distribution of Agents in Mean-Field Games System
Abstract The purpose of this work is to pose and solve the problem to guide a collection of weakly interacting dynamical systems (agents, particles, etc.) to a specified terminal distribution. The framework is that of mean-field and of cooperative games. A terminal cost is used to accomplish the task; we establish that the map between terminal costs and terminal probability distributions is onto. Our approach relies on and extends the theory of optimal mass transport and its generalizations
Mean-Field-Type Games in Engineering
A mean-field-type game is a game in which the instantaneous payoffs and/or
the state dynamics functions involve not only the state and the action profile
but also the joint distributions of state-action pairs. This article presents
some engineering applications of mean-field-type games including road traffic
networks, multi-level building evacuation, millimeter wave wireless
communications, distributed power networks, virus spread over networks, virtual
machine resource management in cloud networks, synchronization of oscillators,
energy-efficient buildings, online meeting and mobile crowdsensing.Comment: 84 pages, 24 figures, 183 references. to appear in AIMS 201
Accelerating Cooperative Planning for Automated Vehicles with Learned Heuristics and Monte Carlo Tree Search
Efficient driving in urban traffic scenarios requires foresight. The
observation of other traffic participants and the inference of their possible
next actions depending on the own action is considered cooperative prediction
and planning. Humans are well equipped with the capability to predict the
actions of multiple interacting traffic participants and plan accordingly,
without the need to directly communicate with others. Prior work has shown that
it is possible to achieve effective cooperative planning without the need for
explicit communication. However, the search space for cooperative plans is so
large that most of the computational budget is spent on exploring the search
space in unpromising regions that are far away from the solution. To accelerate
the planning process, we combined learned heuristics with a cooperative
planning method to guide the search towards regions with promising actions,
yielding better solutions at lower computational costs
Covariance steering in zero-sum linear-quadratic two-player differential games
We formulate a new class of two-person zero-sum differential games, in a
stochastic setting, where a specification on a target terminal state
distribution is imposed on the players. We address such added specification by
introducing incentives to the game that guides the players to steer the join
distribution accordingly. In the present paper, we only address linear
quadratic games with Gaussian target distribution. The solution is
characterized by a coupled Riccati equations system, resembling that in the
standard linear quadratic differential games. Indeed, once the incentive
function is calculated, our problem reduces to a standard one. Tthe framework
developed in this paper extends previous results in covariance control, a fast
growing research area. On the numerical side, problems herein are reformulated
as convex-concave minimax problems for which efficient and reliable algorithms
are available.Comment: 10 page
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