"Distributed Dynamic Reinforcement of Efficient Outcomes in Multiagent Coordination" Georgios Chasparis, Georgia Tech
We focus on the problem of distributed multiagent coordination. When agents have access to limited information about the environment (possibly other agents) and learn (locally) what to play through repeated experimentation, convergence to desirable (global) equilibria (equilibrium selection) might be challenging. To deal with this problem, we introduce a learning adaptation method, similar to reinforcement learning techniques, accompanied with decision rules that are based on feedback control (dynamic reinforcement). This learning framework exploits transient phenomena of the dynamics (off-equilibrium behavior) to reinforce convergence to efficient outcomes when the induced stochastic process has multiple resting points (equilibria). In particular, it is shown that non-efficient outcomes can be destabilized when dynamic reinforcement is applied by even a single agent. The utility of the proposed framework is illustrated in coordination games and distributed network formation, where non-efficient resting points of the stochastic process can be destabilized. In the case of distributed network formation, which is of independent interest, we also illustrate the utility of the proposed learning adaptation method to incorporate multiple design criteria, usually met in topology control for ad-hoc networks, which can reinforce convergence to desirable outcomes.
Two OPTEC professors have been awarded three "Gouden Krijtjes", the yearly teaching awards given by the organization of engineering students (vtk). Prof. Lombaert was awarded the prize for the best course in civil engineering, and Prof. Diehl the prizes for the best professor and the best course in mathematical engineering (where he teaches numerical optimization). They received these awards at the yearly "proffentap" where experienced students taught them how to draft beer professionally.
