GitHub - deepmind/acme: A library of reinforcement learning components and agents

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A library of reinforcement learning components and agents
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README.md
Acme: a research framework for reinforcement learning
Acme is a library of reinforcement learning (RL) building blocks that strives to expose simple, efficient, and readable agents. These agents first and foremost serve both as reference implementations as well as providing strong baselines for algorithm performance. However, the baseline agents exposed by Acme should also provide enough flexibility and simplicity that they can be used as a starting block for novel research. Finally, the building blocks of Acme are designed in such a way that the agents can be run at multiple scales (e.g. single-stream vs. distributed agents).
Getting started
The quickest way to get started is to take a look at the detailed working code examples found in the examples subdirectory. These show how to instantiate a number of different agents and run them within a variety of environments. See the quickstart notebook for an even quicker dive into using a single agent. Even more detail on the internal construction of an agent can be found inside our tutorial notebook . Finally, a full description Acme and its underlying components can be found by referring to the documentation . More background information and details behind the design decisions can be found in our technical report .
NOTE: Acme is first and foremost a framework for RL research written by researchers, for researchers. We use it for our own work on a daily basis. So with that in mind, while we will make every attempt to keep everything in good working order, things may break occasionally. But if so we will make our best effort to fix them as quickly as possible!
Installation
We have tested Acme on Python 3.8 and 3.9. We are working on making it run on the default 3.7 colab kernel. Please bear with us. To get up and running quickly just follow the steps below:
While you can install Acme in your standard python environment, we strongly recommend using a Python virtual environment to manage your dependencies. This should help to avoid version conflicts and just generally make the installation process easier.
python3 -m venv acme source acme/bin/activate pip install --upgrade pip setuptools wheel
While the core dm-acme library can be pip installed directly, the set of dependencies included for installation is minimal. In particular, to run any of the included agents you will also need either JAX or TensorFlow depending on the agent. As a result we recommend installing these components as well, i.e.
pip install dm-acme[jax,tf]