Generating neural architectures from parameter spaces for multi-agent reinforcement learning

We explore a data-driven approach to generating neural network parameters to determine whether generative models can capture the underlying distribution of a collection of neural network checkpoints. We compile a dataset of checkpoints from neural networks trained within the multi-agent reinforcemen...

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Vydané v:	Pattern recognition letters Ročník 185; s. 272 - 278
Hlavní autori:	Artaud, Corentin, De-Silva, Varuna, Pina, Rafael, Shi, Xiyu
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier B.V 01.09.2024
Predmet:	Generative models Multi-agent reinforcement learning Neural networks Parameter generation Transformers Multi-agent reinforcement learning Transformers Parameter generation Generative models Neural networks
ISSN:	0167-8655
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