Acme

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Acme is an AI Agents & Automation tool that provides a research framework for reinforcement learning. It offers a library of components and agents for building and experimenting with RL algorithms.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is acme?

Acme is a comprehensive library developed by Google DeepMind, designed to serve as a research framework for reinforcement learning (RL). It provides a collection of building blocks and agents that are simple, efficient, and readable, serving as both reference implementations and strong baselines for algorithm performance. The framework is flexible enough to be used as a starting point for novel research and supports running agents at various scales, from single-stream to distributed systems. Acme emphasizes ease of use for researchers, offering detailed working code examples, quickstart notebooks, and extensive documentation to help users get started with implementing and experimenting with RL algorithms.

Best used for

Ideal for data scientists and developers who need to build and experiment with reinforcement learning algorithms, develop novel RL research, and benchmark existing agents. Especially valuable for academic and industry researchers looking for a flexible and efficient framework.

Common actions

build RL agents

experiment with RL

research reinforcement learning

implement RL algorithms

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Capabilities

Key features

Reinforcement learning components
Pre-built agents
Distributed agent support
Detailed code examples
Flexible research framework

Target Audience

data scientistdeveloperprofessorstudent

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What programming languages and libraries does Acme support?

Acme is primarily a Python library. While the core library is minimal, it recommends installing dependencies for JAX or TensorFlow depending on the agent you intend to use. It also supports various environments like gym, dm_control, and bsuite.

Is Acme suitable for both single-machine and distributed reinforcement learning experiments?

Yes, Acme's building blocks are designed to support agents running at multiple scales. This includes both single-stream agents for local experimentation and distributed agents for more complex and large-scale reinforcement learning research.

Where can I find examples and documentation to get started with Acme?

You can find detailed working code examples in the 'examples' subdirectory of the GitHub repository. There's also a quickstart notebook for a rapid introduction, a tutorial notebook for internal construction details, and full documentation for comprehensive information.

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