Dueling DQN
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
- 5 April 2016
- Authors
- Ziyu Wang, Tom Schaul, Matteo Hessel, Hado van Hasselt, Marc Lanctot, Nando de Freitas
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.
- Parameters
- 1.7M
- Training data
- tokens
Same parameter count as DQN
Availability
Whether you can obtain the model and run it on your own hardware, which is what decides if any of the graphics-card figures on this page apply.
- Weights
- Closed — provider access only
- Model access
- Unreleased
- Training code
- Unreleased
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
- Confident
"the dueling architecture enables our RL agent to outperform the state-of-the-art on the Atari 2600 domain"
Sources
Where this record came from and when it was last checked.
- Reference
- Dueling Network Architectures for Deep Reinforcement Learning
- Last updated
- 28 November 2025
What the numbers mean
About this model
Dueling DQN was published by Google DeepMind, in the country recorded as United States of America, during April 2016. The category the publisher falls under is 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.
How it was trained
The reason it appears in this catalogue at all: sOTA improvement.
Answers
Dueling DQN — common questions
Dueling DQN— who created it?
It was published by Google DeepMind, based in United States of America, an organisation categorised as industry.
Dueling DQN— when was it released?
It was published in April 2016. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
Dueling DQN— what is it used for?
It works in the domain of Games, and is recorded as handling the task of atari. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
Dueling DQN— 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.
Dueling DQN— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
Dueling DQN— how many parameters does it have?
It has a parameter count of 1.7M. Same parameter count as DQN. That figure is the total, and it is what decides how much memory the model needs — roughly half a gigabyte per billion at the compression most people use.
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.