Game-theoretic Learning And Distributed Optimization In Memoryless Multi-agent Systems
by Tatiana Tatarenko /
2017 / English / EPUB
3 MB Download
This book presents new efficient methods for optimization in
realistic large-scale, multi-agent systems. These methods do not
require the agents to have the full information about the system,
but instead allow them to make their local decisions based only
on the local information, possibly obtained during communication
with their local neighbors. The book, primarily aimed at
researchers in optimization and control, considers three
different information settings in multi-agent systems:
oracle-based, communication-based, and payoff-based. For each of
these information types, an efficient optimization algorithm is
developed, which leads the system to an optimal state. The
optimization problems are set without such restrictive
assumptions as convexity of the objective functions, complicated
communication topologies, closed-form expressions for costs and
utilities, and finiteness of the system’s state space.
This book presents new efficient methods for optimization in
realistic large-scale, multi-agent systems. These methods do not
require the agents to have the full information about the system,
but instead allow them to make their local decisions based only
on the local information, possibly obtained during communication
with their local neighbors. The book, primarily aimed at
researchers in optimization and control, considers three
different information settings in multi-agent systems:
oracle-based, communication-based, and payoff-based. For each of
these information types, an efficient optimization algorithm is
developed, which leads the system to an optimal state. The
optimization problems are set without such restrictive
assumptions as convexity of the objective functions, complicated
communication topologies, closed-form expressions for costs and
utilities, and finiteness of the system’s state space.
