Title:	Efficient Reinforcement Learning in Multi-Agent Systems
DNr:	SNIC 2019/7-63
Project Type:	SNIC Small Compute
Principal Investigator:	Johan Källström <johan.kallstrom@liu.se>
Affiliation:	Linköpings universitet
Duration:	2019-10-21 – 2020-11-01
Classification:	10201
Homepage:	https://www.vinnova.se/p/lvc-simulering-for-forbattrad-traningseffektivitet/
Keywords:

Abstract

This project will investigate efficient methods for reinforcement learning in multi-agent systems, e.g. reward shaping, curriculum learning and hierarchical reinforcement learning. The intended application for the techniques is simulation-based training systems, which adopt a gamified approach. The project will thus use game environments and deep learning frameworks for training of intelligent agents. The project is connected to a research project within the national aeronautical research program,NFFP7.