Project AURIONAI Use-Cases Research & Innovation
Google DeepMind
reinforcement learning

Agent57

Deep reinforcement learning agent that outperforms the standard human benchmark on Atari games. Paper by Badia et al. (2020).

Google DeepMindGoogle DeepMindUK / USA
Figure from the Agent57 paper: Number of games where algorithms are better than the human benchmark throughout training for Agent57 and state-of-the-art baselines on the 57 Atari games.
Levelmodel
Classreinforcement learning
Parametersnot in the notes
Contextnot in the notes
Released30 Mar 2020
Opennessunknown
Runs onnot in the notes
Statusnot in the notes

R&D Topics

Deep reinforcement learning algorithms for controlproduced by

Identity

Developer
Google DeepMind
Family
not in the notes
Released
30 Mar 2020 sourcesannouncement page
Weights published
–
Record checked
12 Oct 2026
Where this comes from
Developer and dates from the research notes. Release date is the vendor's public announcement; weights date is when open weights were published.

Architecture and Size

Architecture
other
Total parameters
– sources
Active parameters
–
Checkpoint parameters
–
Context length
not in the notes
Input
not in the notes
Output
action
Where this comes from
Parameter counts as the developer states them. Where the checkpoint holds a different total, both are shown (register A16).

Openness and Access

Openness
unknown
Licence
not in the notes sources
Access routes
not in the notes
Where this comes from
The licence file governs over registry tags and aggregator labels.

Deployment

Runs on
not in the notes
Footprint
model_class left empty: AURION has no reinforcement learning class yet.
Runtimes
not in the notes
Where this comes from
Deployment tiers and runtimes as named in the notes. Footprint is the notes' figure, not a measurement by AURION.

Indonesia

Indonesian support
not stated
Regional languages
not in the notes
Jakarta hosting
unknown
Languages
not in the notes
Where this comes from
Indonesia fields are carried on every model; unknown is an allowed value.

Benchmark Results

The notes record no benchmark result for this model.

Lineage

The notes record no lineage for this model.

Industry Use Cases

No industry use case links this model.

Sources

  1. arxiv.org/abs/2003.13350 https://arxiv.org/abs/2003.13350

To cite this page: Project AURION, AI Use-Cases Research & Innovation, "Agent57", https://aurion.semai.id/models/google-deepmind-agent57/, records of October 2026.