Double 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
- 2 March 2016
- Authors
- Hado van Hasselt, Arthur Guez, David Silver
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
- 1.5M
- Training data
- tokens
"approximately 1.5M parameters in total"
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
"Table 1 reports summary statistics for this evaluation (under human starts) on the 49 games from Mnih et al. (2015). Double DQN obtains clearly higher median and mean scores. Again"
Sources
Where this record came from and when it was last checked.
- Reference
- Deep Reinforcement Learning with Double Q-Learning
- Last updated
- 28 November 2025
What the numbers mean
Where it came from
Double DQN was published by Google DeepMind, in United States of America, in March 2016. industry is the category the publisher falls under.
It works in Games, and is recorded as doing atari.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
What went into building it
The reason it appears in this catalogue at all is sOTA improvement.
Answers
Double DQN — common questions
What GPU do I need to run Double DQN?
None. Double DQN 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.
Is Double DQN open source?
No. Double DQN has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does Double DQN have?
Double DQN has 1.5M parameters. "approximately 1.5M parameters in total". 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.
Who created Double DQN?
Double DQN was published by Google DeepMind, based in United States of America, categorised as industry.
When was Double DQN released?
Double DQN was published in March 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.
What is Double DQN used for?
Double DQN works in Games, and is recorded as handling 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.
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.