Rainbow 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
- DeepMind
- Organisation type
- Industry
- Country
- United Kingdom of Great Britain and Northern Ireland
- Published
- 6 October 2017
- Authors
- Matteo Hessel, Joseph Modayil, Hado van Hasselt, Tom Schaul, Georg Ostrovski, Will Dabney, Dan Horgan, Bilal Piot, Mohammad Azar, 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.
- Training data
- tokens
The training run
What it physically took to train: which chips, how many, for how long, and what that drew from the wall.
- Chips used
- 1
- Wall-clock time
- 240 hours (10 days)
Single GPU; ~10 hours to reach DQN’s final score (7M frames), ~10 days for full 200M frames run
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
- Unknown
"Our experiments show that the combination provides state-of-the-art performance on the Atari 2600 benchmark, both in terms of data efficiency and final performance."
Sources
Where this record came from and when it was last checked.
- Reference
- Rainbow: Combining Improvements in Deep Reinforcement Learning
- Last updated
- 28 November 2025
What the numbers mean
Background
Rainbow DQN was published by DeepMind, in the country recorded as United Kingdom of Great Britain and Northern Ireland, during October 2017. The publishing organisation is 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
Its inclusion criterion: sOTA improvement.
Answers
Rainbow DQN — common questions
Rainbow DQN— who created it?
It was published by DeepMind, based in United Kingdom of Great Britain and Northern Ireland, an organisation categorised as industry.
Rainbow DQN— when was it released?
It was published in October 2017. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
Rainbow 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.
Rainbow 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.
Rainbow DQN— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
Rainbow DQN— 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.
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.