Efficient Reinforcement Learning in Multi-Agent Systems
Title: Efficient Reinforcement Learning in Multi-Agent Systems
SNIC Project: 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/


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.