1 research outputs found
Adaptive Neighborhood Resizing for Stochastic Reachability in Multi-Agent Systems
We present DAMPC, a distributed, adaptive-horizon and adaptive-neighborhood
algorithm for solving the stochastic reachability problem in multi-agent
systems, in particular flocking modeled as a Markov decision process. At each
time step, every agent calls a centralized, adaptive-horizon model-predictive
control (AMPC) algorithm to obtain an optimal solution for its local
neighborhood. Second, the agents derive the flock-wide optimal solution through
a sequence of consensus rounds. Third, the neighborhood is adaptively resized
using a flock-wide, cost-based Lyapunov function V. This way DAMPC improves
efficiency without compromising convergence. We evaluate DAMPC's performance
using statistical model checking. Our results demonstrate that, compared to
AMPC, DAMPC achieves considerable speed-up (two-fold in some cases) with only a
slightly lower rate of convergence. The smaller average neighborhood size and
lookahead horizon demonstrate the benefits of the DAMPC approach for stochastic
reachability problems involving any controllable multi-agent system that
possesses a cost function.Comment: submitted to conference ATVA 201