Acme
Open-source framework for distributed reinforcement learning
- Status
- Not yet determined
- Last checked
- Unverified
- License
- Open source
- Pricing
- Free tier · from $0
What it is
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.
Acme stands out by offering a modular and efficient framework specifically tailored for large-scale reinforcement learning projects that require distributed training capabilities.
When it fits — and when it doesn’t
A good fit 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.
Look elsewhere if
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.
Known weaknesses
Each one links to the public issue, changelog or docs page it came from, where we have one.
- 01
Performance bottlenecks in large-scale training scenarios
Distributed training can suffer from network latency issues when scaling across multiple nodes
What it does well
Distributed training capabilities
Modular design for flexibility in RL experiments
Efficient and scalable infrastructure
Pricing
- Free tier
- YesOpen source — free to use
- Paid from
- $0
- Model
- Flat rate
- Enterprise plan
- No
Pricing not independently confirmed — check the vendor’s page.
Performance benchmarks
How Fast Is It?
Set up Acme
Setup steps, common gotchas and what to check after 30 days.