In this work we demonstrated that the FOMAML algorithm can be applied to the Hanabi Ad-Hoc Challenge and the cooperative decision making problem it entails.
Oct 21, 2021 · In this work we demonstrate that the First Order Model Agnostic Meta Learning (FOMAML) algorithm trained on the Hanabi Open Agent Dataset ...
In this work, we show empirically that state-of-the-art ZSC algorithms have poor performance when paired with agents trained with different learning methods, ...
META-LEARNING A SOLUTION TO THE HANABI AD-HOC. CHALLENGE. 3.1 Abstract. In this work we demonstrate that the First Order Model Agnostic Meta Learning (FOMAML).
The purpose of this thesis is to investigate how we can learn action conventions by observation in an ad-hoc context.
Missing: Solution Challenge.
Apr 25, 2024 · Meta-Learning a Solution to the Hanabi Ad-Hoc Challenge. FDG 2021: 5:1-5:7. [+][–]. Coauthor network. maximize. Note that this feature is a work ...
We conceptualize the few-shot coordination (FSC) setting for the ad-hoc teamplay challenge in multi-agent reinforcement learning. We accordingly propose the ...
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May 8, 2024 · Meta-Learning a Solution to the Hanabi Ad-Hoc Challenge. FDG 2021: 5:1-5:7. [+][–]. 2010 – 2019. FAQ. see FAQ. What is the meaning of the colors ...
To investigate this issue, we formally defined a framework based on a popular cooperative multi-agent game called Hanabi to evaluate the adaptability of MARL ...
Meta-Learning a Solution to the Hanabi Ad-Hoc Challenge: FDG 2021. 21 views. 3 years ago · Created playlists · 35 · 94.7. Aron Sarmasi · Playlist.