# acme
**Repository Path**: gileyang/acme
## Basic Information
- **Project Name**: acme
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Apache-2.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No
## Statistics
- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-06-05
- **Last Updated**: 2020-12-20
## Categories & Tags
**Categories**: Uncategorized
**Tags**: None
## README
# Acme: A research framework for reinforcement learning
**[Overview](#overview)** | **[Installation](#installation)** |
**[Documentation]** | **[Agents]** | **[Examples]** | **[Paper]**
Acme is a library of reinforcement learning (RL) agents and agent building
blocks. Acme strives to expose simple, efficient, and readable agents, that
serve both as reference implementations of popular algorithms and as strong
baselines, while still providing enough flexibility to do novel research. The
design of Acme also attempts to provide multiple points of entry to the RL
problem at differing levels of complexity.
## Overview
At the highest level Acme exposes a number of agents which can be used simply as
follows:
```python
import acme
# Create an environment and an actor.
environment = ...
actor = ...
# Run the environment loop.
loop = acme.EnvironmentLoop(environment, actor)
loop.run()
```
Acme also tries to maintain this level of simplicity while either diving deeper
into the agent algorithms or by using them in more complicated settings. An
overview of Acme along with more detailed descriptions of its underlying
components can be found by referring to the [documentation][Documentation].
For a quick start, take a look at the more detailed working code examples found
in the [examples][Examples] subdirectory, which also includes a tutorial
notebook to get you started. And finally, for more information on the various
agent implementations available take a look at the [agents][Agents] subdirectory
along with the `README.md` associated with each agent.
## Installation
We have tested `acme` on Python 3.6 & 3.7.
1. **Optional**: We recommend using a
[Python virtual environment](https://docs.python.org/3/tutorial/venv.html)
to manage your dependencies, so as to avoid version conflicts:
```bash
python3 -m venv acme
source acme/bin/activate
pip install --upgrade pip setuptools
```
1. To install `acme` core:
```bash
# Install Acme core dependencies.
pip install dm-acme
# Install Reverb, our replay backend.
pip install dm-acme[reverb]
```
1. To install dependencies for our JAX/TensorFlow-based agents:
```bash
pip install dm-acme[tf]
# and/or
pip install dm-acme[jax]
```
1. Finally, to install environments ([gym], [dm_control], [bsuite]):
```bash
pip install dm-acme[envs]
```
## Citing Acme
If you use Acme in your work, please cite the accompanying
[technical report][Paper]:
```bibtex
@article{hoffman2020acme,
title={Acme: A Research Framework for Distributed Reinforcement Learning},
author={Matt Hoffman and
Bobak Shahriari and
John Aslanides and
Gabriel Barth-Maron and
Feryal Behbahani and
Tamara Norman and
Abbas Abdolmaleki and
Albin Cassirer and
Fan Yang and
Kate Baumli and
Sarah Henderson and
Alex Novikov and
Sergio Gómez Colmenarejo and
Serkan Cabi and
Caglar Gulcehre and
Tom Le Paine and
Andrew Cowie and
Ziyu Wang and
Bilal Piot and
Nando de Freitas},
year={2020},
journal={arXiv preprint arXiv:2006.00979},
url={https://arxiv.org/abs/2006.00979},
}
```
[Documentation]: docs/index.md
[Examples]: examples/
[Agents]: acme/agents/
[Reverb]: https://github.com/deepmind/reverb
[Paper]: https://arxiv.org/abs/2006.00979
[gym]: https://github.com/openai/gym
[dm_control]: https://github.com/deepmind/dm_control
[bsuite]: https://github.com/deepmind/bsuite