enn_acme defines an interface for designing RL agents that combines:
Before diving into enn_acme, you should first read the tutorials for both of these underlying libraries.
enn_acme is really a thin convenience layer designed to expose certain "key concepts" in agent design.
We outline the key high-level interfaces for our code in base.py:
enn: epistemic neural network = knowledge representation.EnnPlanner: selects actions given (params, observation) for ENN.LossFn: tells the agent how to evaluate loss of a given batch of data.
The best place to get started is in our colab tutorial.
Here, you can find a quick rundown of our main types in the code, and find an example of running a version of bootstrapped DQN on bsuite environments.
Once you have had a look at this colab, you might want to have a look at our example experiments/.
We have tested enn_acme on Python 3.7. To install the dependencies:
-
Optional: We recommend using a Python virtual environment to manage your dependencies, so as not to clobber your system installation:
python3 -m venv enn_acme source enn_acme/bin/activate pip install --upgrade pip setuptools -
Install
enn_acmedirectly from github:pip install git+https://github.com/deepmind/enn_acme
More examples can be found in the colab tutorial.
- Optional: run the tests by executing
./test.shfromenn_acmeroot directory.
If you use enn_acme in your work, please cite the enn paper and acme report:
@article{osband2022epistemic,
title={Epistemic neural networks},
author={Osband, Ian and Wen, Zheng and Asghari, Seyed Mohammad and Dwaracherla, Vikranth and Ibrahimi, Morteza and Lu, Xiuyuan and Van Roy, Benjamin},
journal={arXiv preprint arXiv:2107.08924},
year={2022}
}@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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