Rational DQN Average
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
- TU Darmstadt
- Organisation type
- Academia
- Country
- Germany
- Published
- 18 February 2021
- Authors
- Q Delfosse, P Schramowski, A Molina
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
See figure 7
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
- Open (non-commercial)
"To get the trained agents, please contact Quentin Delfosse" no clear license https://github.com/ml-research/rational_rl/
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
- Citations
- 26
They don't claim absolute SOTA "We demonstrate that equipping popular algorithms with (joint) rational activations leads to consistent improvements on Atari games, notably making DQN competitive to DDQN and Rainbow"
Sources
Where this record came from and when it was last checked.
- Reference
- Recurrent Rational Networks
- Last updated
- 25 May 2026
What the numbers mean
Where it came from
Rational DQN Average was published by TU Darmstadt, in Germany, in February 2021. It comes out of academia.
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
It is tracked in the underlying dataset for one reason in particular: sOTA improvement.
Answers
Rational DQN Average — common questions
Is Rational DQN Average open source?
No. Rational DQN Average has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does Rational DQN Average have?
Rational DQN Average has 1.7M parameters. See figure 7. 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 Rational DQN Average?
Rational DQN Average was published by TU Darmstadt, based in Germany, categorised as academia.
When was Rational DQN Average released?
Rational DQN Average was published in February 2021. 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 Rational DQN Average used for?
Rational DQN Average works in Games, and is recorded as handling atari. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
What GPU do I need to run Rational DQN Average?
None. Rational DQN Average 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.
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.