https://github.com/epignatelli/discovering-reinforcement-learning-algorithms

A Jax/Stax implementation of the general meta learning paper: Oh, J., Hessel, M., Czarnecki, W.M., Xu, Z., van Hasselt, H.P., Singh, S. and Silver, D., 2020. Discovering reinforcement learning algorithms. Advances in Neural Information Processing Systems, 33.
https://github.com/epignatelli/discovering-reinforcement-learning-algorithms

actor-critic general-meta-learning jax lstm meta-learning paper-implementations paper-with-code policy-gradient reinforcement-learning reinforcement-learning-algorithms rnn stax

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A Jax/Stax implementation of the general meta learning paper: Oh, J., Hessel, M., Czarnecki, W.M., Xu, Z., van Hasselt, H.P., Singh, S. and Silver, D., 2020. Discovering reinforcement learning algorithms. Advances in Neural Information Processing Systems, 33.

• Host: GitHub
• URL: https://github.com/epignatelli/discovering-reinforcement-learning-algorithms
• Owner: epignatelli
• License: apache-2.0
• Created: 2020-12-14T14:46:54.000Z (almost 5 years ago)
• Default Branch: main
• Last Pushed: 2020-12-22T18:32:22.000Z (almost 5 years ago)
• Last Synced: 2023-03-10T12:12:13.268Z (over 2 years ago)
• Topics: actor-critic, general-meta-learning, jax, lstm, meta-learning, paper-implementations, paper-with-code, policy-gradient, reinforcement-learning, reinforcement-learning-algorithms, rnn, stax
• Language: Python
• Homepage: https://proceedings.neurips.cc/paper/2020/file/0b96d81f0494fde5428c7aea243c9157-Paper.pdf
• Size: 80.1 KB
• Stars: 11
• Watchers: 1
• Forks: 3
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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# Discovering reinforcement learning algorithms

A jax/stax implementation of the NeurIPS 2020 paper: _Discovering reinforcement learning algorithms_ [[1]](https://proceedings.neurips.cc/paper/2020/file/0b96d81f0494fde5428c7aea243c9157-Paper.pdf)

The agent at `lpg.agent.py` implements the `bsuite.baseline.base.Agent` interface.

The `lpg/environments/*.py` interfaces with a `dm_env.Environment`.

We wrap the [gym-atari](https://github.com/openai/gym) suite using the `bsuite.utils.gym_wrapper.DMEnvFromGym` adapter into a `dqn.AtariEnv` to implement historical observations and actions repeat.

## Installation

To run the algorithm on a GPU, I suggest to [install](https://github.com/google/jax#pip-installation) the gpu version of `jax` [[4]](https://github.com/google/jax). You can then install this repo using [Anaconda python](https://www.anaconda.com/products/individual) and [pip](https://pip.pypa.io/en/stable/installing/).

```sh

conda env create -n lpg

conda activate lpg

pip install git+https://github.com/epignatelli/discovering-reinforcement-learning-algorithms

```

## References

[1] [_Oh, J., Hessel, M., Czarnecki, W.M., Xu, Z., van Hasselt, H.P., Singh, S. and Silver, D., 2020. Discovering reinforcement learning algorithms. Advances in Neural Information Processing Systems, 33._](https://proceedings.neurips.cc/paper/2020/file/0b96d81f0494fde5428c7aea243c9157-Paper.pdf)