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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Multi-Objective Reinforcement Learning algorithms implementations.
| Date | Stars |
|---|---|
| 2026-07-31 | 532 |
| 2026-08-01 | 532 |
| 2026-08-02 | 532 |
| 2026-08-06 | 533 |
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growth rate 0.00%/day
[](https://www.repostatus.org/#active)  [](https://github.com/LucasAlegre/morl-baselines/blob/main/LICENSE) [](https://discord.gg/ygmkfnBvKA) [](https://pre-commit.com/) [](https://github.com/psf/black) [](https://pycqa.github.io/isort/) <img src="docs/_static/_images/mo_cheetah.gif" alt="Multiple policies" align="right" width="50%"/> # MORL-Baselines <!-- start elevator-pitch --> MORL-Baselines is a library of Multi-Objective Reinforcement Learning (MORL) algorithms. This repository aims to contain reliable MORL algorithms implementations in PyTorch. It strictly follows [MO-Gymnasium](https://github.com/Farama-Foundation/mo-gymnasium) API, which differs from the standard [Gymnasium](https://github.com/Farama-Foundation/Gymnasium) API only in that the environment returns a numpy array as the reward. For details on multi-objective MDPs (MOMDPs) and other MORL definitions, we suggest reading [A practical guide to multi-objective reinforcement learning and planning](https://link.springer.com/article/10.1007/s10458-022-09552-y). An overview of some techniques used in various MORL algorithms is also provided in [Multi-Objective Reinforcement Learning Based on Decomposition: A Taxonomy and Framework](https://doi.org/10.1613/jair.1.15702). A tutorial on MO-Gymnasium and MORL-Baselines is also available: [](https://colab.research.google.com/drive/1ByjuUp8-CJeh1giPOACqPGiglPxDnlSq?usp=sharing) <!-- end elevator-pitch --> ## Features <!-- start features --> * Single and multi-policy algorithms under both SER and ESR criteria are implemented. * All algorithms follow the [MO-Gymnasium](https://www.github.com/Farama-Foundation/mo-gymnasium) API. * Performances are automatically reported in [Weights and Biases](https://wandb.ai/) dashboards. * Linting and formatting are enforced by pre-commit hooks. * Code is well documented. * All algorithms are automatically tested. * Utility functions are provided e.g. pareto pruning, experience buffers, etc. * Performances have been tested and reported in a reproducible manner. * Hyperparameter optimization available. <!-- end features --> ## Implemented Algorithms <!-- start algos-list --> | **Name** | Single/Multi-policy | ESR/SER | Observation space | Action space | Paper | |-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------------------|-----------------------------|-----------------------|-----------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------| | [GPI-LS + GPI-PD](https://github.com/LucasAlegre/morl-baselines/blob/main/morl_baselines/multi_policy/gpi_pd/gpi_pd.py) | Multi |
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Lucas Alegre · Institute of Informatics - UFRGS · Brazil
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Ikko Eltociear Ashimine · Japan
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:ed75032580e4dee1, topic:reinforcement-learning, topic:rl, topic:gymnasium