Acme
Open-source framework for distributed reinforcement learning
Pricing
Free tier
Flat rate
Adoption
→StableLicense
Open Source
Data freshness
UnverifiedOverview
What is Acme?
Acme is an open-source framework that simplifies the process of building and training reinforcement learning agents, offering a scalable solution for researchers and developers.
Key differentiator
“Acme stands out by offering a modular and efficient framework specifically tailored for large-scale reinforcement learning projects that require distributed training capabilities.”
Capability profile
Capability Radar
Honest assessment
Strengths & Weaknesses
↑ Strengths
↓ Weaknesses
API requires Python-specific patterns, TypeScript SDK is community-maintained
v0.1 to v0.2 migration required rewriting chain definitions
Primary development and documentation focus on Python, with no official support for other languages
Distributed training can suffer from network latency issues when scaling across multiple nodes
Fit analysis
Who is it for?
✓ Best for
Teams working on large-scale reinforcement learning projects that require distributed training capabilities.
Academics and researchers who need a flexible framework to conduct complex RL experiments.
✕ Not a fit for
Projects with limited computational resources, as Acme is designed for scalable and distributed systems.
Beginners in machine learning who are not familiar with reinforcement learning concepts.
Cost structure
Pricing
Free Tier
Available
Open source — free to use
Starts at
$0
Model
Flat rate
Enterprise
None
Performance benchmarks
How Fast Is It?
Ecosystem
Relationships
Alternatives
Next step
Get Started with Acme
Step-by-step setup guide with code examples and common gotchas.