506 research outputs found
Gradient Dynamics in Linear Quadratic Network Games with Time-Varying Connectivity and Population Fluctuation
In this paper, we consider a learning problem among non-cooperative agents
interacting in a time-varying system. Specifically, we focus on repeated linear
quadratic network games, in which the network of interactions changes with time
and agents may not be present at each iteration. To get tractability, we assume
that at each iteration, the network of interactions is sampled from an
underlying random network model and agents participate at random with a given
probability. Under these assumptions, we consider a gradient-based learning
algorithm and establish almost sure convergence of the agents' strategies to
the Nash equilibrium of the game played over the expected network.
Additionally, we prove, in the large population regime, that the learned
strategy is an -Nash equilibrium for each stage game with high
probability. We validate our results over an online market application.Comment: 8 pages, 2 figures, Extended version of the original paper to appear
in the proceedings of the 2023 IEEE Conference on Decision and Control (CDC
Joint Coverage and Power Control in Highly Dynamic and Massive UAV Networks: An Aggregative Game-theoretic Learning Approach
Unmanned aerial vehicles (UAV) ad-hoc network is a significant contingency
plan for communication after a natural disaster, such as typhoon and
earthquake. To achieve efficient and rapid networks deployment, we employ
noncooperative game theory and amended binary log-linear algorithm (BLLA)
seeking for the Nash equilibrium which achieves the optimal network
performance. We not only take channel overlap and power control into account
but also consider coverage and the complexity of interference. However,
extensive UAV game theoretical models show limitations in post-disaster
scenarios which require large-scale UAV network deployments. Besides, the
highly dynamic post-disaster scenarios cause strategies updating constraint and
strategy-deciding error on UAV ad-hoc networks. To handle these problems, we
employ aggregative game which could capture and cover those characteristics.
Moreover, we propose a novel synchronous payoff-based binary log-linear
learning algorithm (SPBLLA) to lessen information exchange and reduce time
consumption. Ultimately, the experiments indicate that, under the same
strategy-deciding error rate, SPBLLA's learning rate is manifestly faster than
that of the revised BLLA. Hence, the new model and algorithm are more suitable
and promising for large-scale highly dynamic scenarios
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