This repository contains accompanying code for the "Discovering State-of-the-art Reinforcement Learning Algorithms" Nature publication.

It provides a minimal JAX harness for the DiscoRL setup together with the original meta-learned weights for the Disco103 discovered update rule.

The harness supports both:

• Meta-evaluation: training an agent using the Disco103 discovered RL update rule, using the colabs/eval.ipynb notebook and

• Meta-training: meta-learning a RL update rule from scratch or from a pre-existing checkpoint, using the colabs/meta_train.ipynb notebook

Note that it will not be actively maintained moving forward.

Set up a Python virtual environment and install the package:

python3 -m venv disco_rl_venv
source disco_rl_venv/bin/activate
pip install git+https://github.com/google-deepmind/disco_rl.git

The package can also be installed from colab:

!pip install git+https://github.com/google-deepmind/disco_rl.git

The code is structured as follows:

• environments/ contains the general interface for the environments that can be used with the provided harness, and two implementations of Catch: a CPU-based one and jittable;

• networks/ includes a simple MLP network and LSTM-based components of the DiscoRL models, all implemented in Haiku;

• update_rules/ has implementations of the discovered rules, actor-critic, and policy gradient;

• value_fns/ contains value-function related utilities;

• types.py, utils.py, optimizers.py implement a basic functionality for the harness;

• agent.py is a generic implementation of an RL agent which uses the update rule's API for training, hence it is compatible with all the rules from update_rules/.

Detailed examples of usage can be found in the colabs above.

Please cite the original Nature paper:

@Article{DiscoRL2025,
  author  = {Oh, Junhyuk and Farquhar, Greg and Kemaev, Iurii and Calian, Dan A. and Hessel, Matteo and Zintgraf, Luisa and Singh, Satinder and van Hasselt, Hado and Silver, David},
  journal = {Nature},
  title   = {Discovering State-of-the-art Reinforcement Learning Algorithms},
  year    = {2025},
  doi     = {10.1038/s41586-025-09761-x}
}

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