  In this paper, we propose a framework for solving a single-agent task by
using multiple agents, each focusing on different aspects of the task. This
approach has two main advantages: 1) it allows for training specialized agents
on different parts of the task, and 2) it provides a new way to transfer
knowledge, by transferring trained agents. Our framework generalizes the
traditional hierarchical decomposition, in which, at any moment in time, a
single agent has control until it has solved its particular subtask. We
illustrate our framework with empirical experiments on two domains.
