@article{vanseijen2017improving,
author = {van Seijen, Harm and Fatemi, Mehdi and Romoff, Josh and Laroche, Romain},
title = {Improving Scalability of Reinforcement Learning by Separation of Concerns},
year = {2017},
month = {March},
abstract = {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.},
url = {http://approjects.co.za/?big=en-us/research/publication/improving-scalability-reinforcement-learning-separation-concerns/},
journal = {arXiv},
edition = {arXiv},
}