learning the task allocation game
The distributed task allocation problem occurs in domains like web services, the grid, and other distributed systems. In this problem, the system consists of servers and mediators. Servers execute tasks and may differ in their capabilities, e.g. one server may take more time than the other in executing the same task. Mediators act on behalf of users, which can potentially be other mediators, and are responsible for receiving tasks from users and allocating them to servers.This work introduces a new gradient ascent learning algorithm that outperforms state of the art multiagent learners on this problem. We experimentally show that our algorithm converges faster and is less sensitive to tuning parameters than other algorithms. We also provide an informal proof that WPL has the same convergence guarantee as the best known algorithm, GIGA-WoLF. We also show that our algorithm converges in Jordan's and Shapley's games where many other algorithms fail to converge. Finally, we verify the practicality of our algorithm in the distributed task allocation domain, comparing its performance to an optimal global solution.
task allocation multiagent systems reinforcement learning markov decision process