Advantage Learning
No estimate
No hardware requirements for this model
The weights for this model have not been published, so it cannot be downloaded or run on your own hardware at any size. It is reachable only through its provider, and no graphics card changes that.
On record
Full specification
Everything on record for this model. Most of it describes how it was trained rather than how it runs — useful context for judging how much work went into it, and how it compares with models built at a different scale.
Origin
Who built this model, where, and when it was published.
- Organisation
- Google DeepMind
- Organisation type
- Industry
- Country
- United States of America
- Published
- 15 December 2015
- Authors
- MG Bellemare, G Ostrovski, A Guez
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Games
- Task
- Atari
Size
How large the model is and how much data it was trained on. Parameters are the figure that decides whether it fits on a given graphics card.
- Training data
- 100,000,000 tokens
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Why it is tracked
- SOTA improvement
- Record confidence
- Unknown
- Citations
- 166
"We use our operators to obtain state-of-the-art empirical results on the Arcade Learning Environment (Bellemare et al. 2013)."
Sources
Where this record came from and when it was last checked.
- Reference
- Increasing the Action Gap: New Operators for Reinforcement Learning
- Last updated
- 25 May 2026
What the numbers mean
About this model
Advantage Learning was published by Google DeepMind, in the country recorded as United States of America, during December 2015. It comes out of an organisation categorised as industry.
It works in the domain of Games, and is recorded as performing the task of atari.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
Training and provenance
The training set ran to roughly 100,000,000 tokens of text.
The reason it appears in this catalogue at all: sOTA improvement.
Answers
Advantage Learning — common questions
Advantage Learning— how many parameters does it have?
No parameter count has been published for it, which is why no memory or speed figure appears on this page.
Advantage Learning— who created it?
It was published by Google DeepMind, based in United States of America, an organisation categorised as industry.
Advantage Learning— when was it released?
It was published in December 2015. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
Advantage Learning— what is it used for?
It works in the domain of Games, and is recorded as handling the task of atari. A model can carry several of each, so these are the areas it was built for rather than a limit on what it will attempt.
Advantage Learning— what GPU do I need to run it?
None. This is a closed model — its weights were never published, so it cannot be downloaded or run on your own hardware at any price. It is reachable only through its provider.
Advantage Learning— is it open source?
The licensing was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
The other direction
Looking at it from the other side?
This page starts from the model. If you already own a card and want to know everything it will run, start from the hardware instead.