In this article, we present the design of a team of heterogeneous, centimeter-scale robots that collaborate to map and explore unknown environments. The robots, called Millibots, a...
Robert Grabowski, Luis E. Navarro-Serment, Christi...
— We address the problem of placing a sensor network so as to minimize the uncertainty in estimating the position of targets. The novelty of our formulation is in the sensing mod...
Real world multiagent coordination problems are important issues for reinforcement learning techniques. In general, these problems are partially observable and this characteristic ...
A collective of agents often needs to maximize a “world utility” function which rates the performance of an entire system, while subject to communication restrictions among th...
In this paper, we present a novel multi-agent learning paradigm called team-partitioned, opaque-transition reinforcement learning (TPOT-RL). TPOT-RL introduces the concept of usin...