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Estimation Network Design for Games under Partial-decision Information
Multi-agent decision problems are typically solved via distributed iterative algorithms, where the agents only communicate between themselves on a peerto- peer network. Each agent usually maintains a copy of each decision variable, while agreement among the local copies is enforc
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We study generalized games with full row rank equality coupling constraints and we provide a strikingly simple proof of strong monotonicity of the associated KKT operator. This allows us to show linear convergence to a variational equilibrium of the resulting primal-dual pseudo-g
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We design a distributed algorithm for learning Nash equilibria over time-varying communication networks in a partial-decision information scenario, where each agent can access its own cost function and local feasible set, but can only observe the actions of some neighbors. Our al
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We address the generalized Nash equilibrium seeking problem in a partial-decision information scenario, where each agent can only exchange information with some neighbors, although its cost function possibly depends on the strategies of all agents. The few existing methods build
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The distributed dual ascent is an established algorithm to solve strongly convex multi-agent optimization problems with separable cost functions, in the presence of coupling constraints. In this letter, we study its asynchronous counterpart. Specifically, we assume that each agen
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We consider strongly monotone games with convex separable coupling constraints, played by dynamical agents, in a partial-decision information scenario. We start by designing continuous-time fully distributed feedback controllers, based on consensus and primal–dual gradient dynami
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We consider Nash equilibrium problems in a partial-decision information scenario, where each agent can only exchange information with some neighbors, while its cost function possibly depends on the strategies of all agents. We characterize the relation between several monotonicit
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We consider the Nash equilibrium problem in a partial-decision information scenario. Specifically, each agent can only receive information from some neighbors via a communication network, while its cost function depends on the strategies of possibly all agents. In particular, whi
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We consider a system of single- or double-integrator agents playing a generalized Nash game over a network, in a partial-information scenario. We address the generalized Nash equilibrium seeking problem by designing a fully-distributed dynamic controller, based on continuous-time
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We address the Nash equilibrium problem in a partial-decision information scenario, where each agent can only observe the actions of some neighbors, while its cost possibly depends on the strategies of other agents. Our main contribution is the design of a fully-distributed, sing
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The topic of this dissertation is the distributed computation of Generalized Nash Equilibria (GNEs) in multi-agent games with network structure. In particular, we design and analyze algorithms in the partial-decision information scenario (also named fully-distributed algorithms),
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Contributed
2 records found
Distributed Model Predictive Control for Multi-Vehicle Autonomous Driving
Cooperative vs. Non-cooperative Control
To make the cooperation within a physical human-robot team as efficient as possible, the team members must be able to co-adapt.
We developed and evaluated a robot that adapts to a human, using an adaptation strategy, in such a way as to guide the co-adaptation to have a positive
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