Deep Deterministic Policy Gradients
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
- 9 September 2015
- Authors
- TP Lillicrap, JJ Hunt, A Pritzel, N Heess, T Erez
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Robotics
- Task
- Robotic manipulation, Self-driving car
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
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
- Highly cited
- Record confidence
- Unknown
- Citations
- 15,374
Sources
Where this record came from and when it was last checked.
- Reference
- Continuous control with deep reinforcement learning
- Last updated
- 25 May 2026
What the numbers mean
Where it came from
Deep Deterministic Policy Gradients was published by Google DeepMind, in the country recorded as United States of America, during September 2015. It comes out of an organisation categorised as industry.
It works in the domain of Robotics, and is recorded as performing the task of robotic manipulation, Self-driving car.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
Training and provenance
Its inclusion criterion: highly cited.
Answers
Deep Deterministic Policy Gradients — common questions
Deep Deterministic Policy Gradients— 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.
Deep Deterministic Policy Gradients— 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.
Deep Deterministic Policy Gradients— who created it?
It was published by Google DeepMind, based in United States of America, an organisation categorised as industry.
Deep Deterministic Policy Gradients— when was it released?
It was published in September 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.
Deep Deterministic Policy Gradients— what is it used for?
It works in the domain of Robotics, and is recorded as handling the task of robotic manipulation, Self-driving car. These are the areas it was designed around; they describe intent rather than a hard boundary.
Deep Deterministic Policy Gradients— 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.
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.