Multi-agent reinforcement learning: weighting and partitioning.

This article addresses weighting and partitioning, in complex reinforcement learning tasks, with the aim of facilitating learning. The article presents some ideas regarding weighting of multiple agents and extends them into partitioning an input/state space into multiple regions with differential weighting in these regions, to exploit differential characteristics of regions and differential characteristics of agents to reduce the learning complexity of agents (and their function approximators) and thus to facilitate the learning overall. It analyzes, in reinforcement learning tasks, different ways of partitioning a task and using agents selectively based on partitioning. Based on the analysis, some heuristic methods are described and experimentally tested. We find that some off-line heuristic methods perform the best, significantly better than single-agent models.