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Main Authors:	Perez-Liebana, Diego, Hofmann, Katja, Mohanty, Sharada Prasanna, Kuno, Noboru, Kramer, Andre, Devlin, Sam, Gaina, Raluca D., Ionita, Daniel
Format:	Preprint
Published:	2019
Subjects:	Artificial Intelligence
Online Access:	https://arxiv.org/abs/1901.08129
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author	Perez-Liebana, Diego Hofmann, Katja Mohanty, Sharada Prasanna Kuno, Noboru Kramer, Andre Devlin, Sam Gaina, Raluca D. Ionita, Daniel
author_facet	Perez-Liebana, Diego Hofmann, Katja Mohanty, Sharada Prasanna Kuno, Noboru Kramer, Andre Devlin, Sam Gaina, Raluca D. Ionita, Daniel
contents	Learning in multi-agent scenarios is a fruitful research direction, but current approaches still show scalability problems in multiple games with general reward settings and different opponent types. The Multi-Agent Reinforcement Learning in MalmÖ (MARLÖ) competition is a new challenge that proposes research in this domain using multiple 3D games. The goal of this contest is to foster research in general agents that can learn across different games and opponent types, proposing a challenge as a milestone in the direction of Artificial General Intelligence.
format	Preprint
id	arxiv_https___arxiv_org_abs_1901_08129
institution	arXiv
publishDate	2019
record_format	arxiv
spellingShingle	The Multi-Agent Reinforcement Learning in MalmÖ (MARLÖ) Competition Perez-Liebana, Diego Hofmann, Katja Mohanty, Sharada Prasanna Kuno, Noboru Kramer, Andre Devlin, Sam Gaina, Raluca D. Ionita, Daniel Artificial Intelligence Learning in multi-agent scenarios is a fruitful research direction, but current approaches still show scalability problems in multiple games with general reward settings and different opponent types. The Multi-Agent Reinforcement Learning in MalmÖ (MARLÖ) competition is a new challenge that proposes research in this domain using multiple 3D games. The goal of this contest is to foster research in general agents that can learn across different games and opponent types, proposing a challenge as a milestone in the direction of Artificial General Intelligence.
title	The Multi-Agent Reinforcement Learning in MalmÖ (MARLÖ) Competition
topic	Artificial Intelligence
url	https://arxiv.org/abs/1901.08129

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// Anecdotal testing Today and Yesterday seems to indicate destroy() // is called by the garbage collector and everything is good. // Something to keep in mind though. return 0; } /** * Write function that is called when session data is to be saved. * * @param string $sessId The current session ID * @param string $data The session data to write * * @return bool */ public function write($sessId, $data): bool { if ($this->writesDisabled) { return true; } return $this->saveSession($sessId, $data); } /** * A function that is called internally when session data is to be saved. * * @param string $sessId The current session ID * @param string $data The session data to write * * @return bool */ abstract protected function saveSession($sessId, $data): bool; }

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