A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf
.
Extending World Models for Multi-Agent Reinforcement Learning in MALMÖ
2018
Artificial Intelligence and Interactive Digital Entertainment Conference
Recent work in (deep) reinforcement learning has increasingly looked to develop better agents for multi-agent/multitask scenarios as many successes have already been seen in the usual single-task single-agent setting. In this paper, we propose a solution for a recently released benchmark which tests agents in such scenarios, namely the MARLÖ competition. Following the 2018 Jeju Deep Learning Camp, we consider a combined approach based on various ideas generated during the camp as well as
dblp:conf/aiide/ChockalingamSBG18
fatcat:g63hnabnwzejtjw4lxms5dwhc4