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 United States of America, in September 2015. It comes out of industry.
It works in Robotics, and is recorded as doing 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 is highly cited.
Answers
Deep Deterministic Policy Gradients — common questions
Is Deep Deterministic Policy Gradients open source?
The licensing for Deep Deterministic Policy Gradients was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
How many parameters does Deep Deterministic Policy Gradients have?
No parameter count has been published for Deep Deterministic Policy Gradients, which is why no memory or speed figure appears on this page.
Who created Deep Deterministic Policy Gradients?
Deep Deterministic Policy Gradients was published by Google DeepMind, based in United States of America, categorised as industry.
When was Deep Deterministic Policy Gradients released?
Deep Deterministic Policy Gradients 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.
What is Deep Deterministic Policy Gradients used for?
Deep Deterministic Policy Gradients works in Robotics, and is recorded as handling robotic manipulation, Self-driving car. These are the areas it was designed around; they describe intent rather than a hard boundary.
What GPU do I need to run Deep Deterministic Policy Gradients?
None. Deep Deterministic Policy Gradients 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.