内容简介:||
Acme: A research framework for reinforcement learning
|| 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:
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 .
For a quick start, take a look at the more detailed working code examples found in the 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 subdirectory along with the README.md
associated with each agent.
Installation
We support Python 3.6 and 3.7.
To install acme
core:
# Install Acme core dependencies. pip install dm-acme # Install Reverb, our replay backend. pip install dm-acme[reverb]
To install dependencies for our JAX/TensorFlow-based agents:
pip install dm-acme[tf] # and/or pip install dm-acme[jax]
Finally, to install environments (gym, dm_control, bsuite):
pip install dm-acme[envs]
Citing Acme
If you use Acme in your work, please cite the accompanying technical report :
@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}, }
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