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benchmarl

BenchMARL is a library for benchmarking Multi-Agent Reinforcement Learning (MARL). BenchMARL allows to quickly compare different MARL algorithms, tasks, and models while being systematically grounded in its two core tenets: reproducibility and standardization.
PyPI GitHub
Downloads
Day
79
Week
189
Month
837
Stars
617
Forks
128
Issues
28
Version
1.5.2

Package Insights

What's this?
Score breakdown - each component scores 0–100, weighted by percentage
Maintenance
70
Popularity
60
Adoption
29
Community
75
How the maintenance status was determined
Last commit
88 days ago
Monthly downloads
837
Active
Activity within 30 days
• Stable
Activity within 31-180 days
Minimal
Activity within 181-365 days, or no activity data
At Risk
No activity for > 365 days, but > 10,000 monthly downloads
Archived
Repository flagged as archived on GitHub
How the popularity category was determined
Monthly downloads
837
Stars
617
Stars per 1k downloads
737.16
Industry Standard
> 10,000,000 monthly downloads
Hidden Gem
< 0.5 stars per 1k downloads and > 100,000 monthly downloads
Community Favorite
> 5 stars per 1k downloads
Rising Star
Balanced ratio of stars to downloads
• Unknown
Fewer than 1,000 monthly downloads, or no star data
Needs at least 1,000 monthly downloads to classify popularity.
How download momentum is calculated
Week daily avg
27
Month daily avg
28
Momentum
—
((week_daily_avg - month_daily_avg) / month_daily_avg) * 100
Momentum not reported for packages with < 5,000 monthly downloads — percentages are noisy at low volume.
Python version support tiers
requires_python
Not specified
Cutting Edge
Minimum supported version is 3.12 or newer
Modern
Minimum supported version is 3.9, 3.10, or 3.11
Legacy
Supports 3.8 or older (or Python 2)
• Unknown
No requires_python specified or unparseable
Package does not declare a minimum Python version.
License categories
License
Not specified
Permissive
MIT, Apache, BSD, ISC, Unlicense, CC0, etc.
Copyleft
GPL, LGPL, AGPL, MPL, EPL, etc.
Proprietary
Commercial, all rights reserved, custom EULAs
• Unknown
No license specified, or none of the above match
Package does not declare a license.
How the release cadence was determined
Last release
88 days ago
Total releases
14
Releases/year
5.5
Rapid
Last release within 365 days and > 12 releases/year
• Regular
Last release within 365 days
Slow
Last release 366-730 days ago
Stalled
Last release > 730 days ago, or no releases
Unknown
No usable release date available
How contributor risk was determined
Contributors
58
Monthly downloads
837
High Load
≤ 2 contributors and ≥ 50,000 monthly downloads
Moderate Load
< 10 contributors and ≥ 500,000 monthly downloads
Low Load
< 25 contributors and ≥ 5,000,000 monthly downloads, or sole maintainer with ≥ 10,000 monthly downloads
• Balanced
Enough contributors for the package's popularity
Unknown
No contributor data available
Engagement
68
 
Issue Burden
45.4
per 1k stars
Discussions
0.0
per 1k stars
Fork Ratio
45.3%
contrib / forks
Stale Release
No
 
PR Velocity
Quiet
 

Weekly Downloads

GitHub Stars

Not all downloads include detailed metadata. Breakdowns reflect the subset that does.

Downloads by OS

Linux 5
Windows 1

Python Versions

3.12
50.0% 3
3.11
33.3% 2
3.10
16.7% 1

Top Countries

1. US US
3
2. AE AE
1
3. CN CN
1
4. SG SG
1

Dependencies

6
6 optional dependencies

Used By

0
Not used by any tracked packages

Package Information

License
Unknown
First Release
October 12, 2023
Last Release
2 months ago
Last Commit
2 months ago
Latest Version
1.5.2
Total Releases
14
Contributors
58
Open PRs
6
Archived
No
Author
Matteo Bettini
Topics
benchmark machine-learning marl multi-agent multi-agent-reinforcement-learning pytorch reinforcement-learning rl robotics torch
Tracking Since
January 14, 2026
Data Updated
May 3, 2026
Data Freshness
Fresh

Links