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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.
NeurIPS 2023: Safe Policy Optimization: A benchmark repository for safe reinforcement learning algorithms
| Date | Stars |
|---|---|
| 2026-07-31 | 415 |
| 2026-08-01 | 415 |
| 2026-08-02 | 415 |
| 2026-08-06 | 415 |
Today
— stars today
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Momentum
0.0
growth rate 0.00%/day
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<img src="assets/logo.png" width="75%"/>
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[](https://github.com/PKU-Alignment)
[](#license)
[](https://codecov.io/gh/PKU-Alignment/Safe-Policy-Optimization)
[](https://safe-policy-optimization.readthedocs.io/en/latest/?badge=latest)
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## Citing Safe Policy Optimization
If you find Safe Policy Optimization useful, please cite it in your publications.
```bibtex
@article{ji2023safety,
title={Safety-Gymnasium: A Unified Safe Reinforcement Learning Benchmark},
author={Ji, Jiaming and Zhang, Borong and Zhou, Jiayi and Pan, Xuehai and Huang, Weidong and Sun, Ruiyang and Geng, Yiran and Zhong, Yifan and Dai, Juntao and Yang, Yaodong},
journal={arXiv preprint arXiv:2310.12567},
year={2023}
}
```
**What's New**:
- Feel free to open an [issue](https://github.com/PKU-Alignment/Safe-Policy-Optimization/issues) if you encounter any problem in Mac or Windows.
- We have release [Documentation](https://safe-policy-optimization.readthedocs.io).
- The **benchmark results** of SafePO can be viewed at [Wandb Report](https://safe-policy-optimization.readthedocs.io/en/latest/algorithms/general.html).
**Safe Policy Optimization (SafePO)** is a comprehensive algorithm benchmark for Safe Reinforcement Learning (Safe RL). It provides RL research community with a unified platform for processing and evaluating algorithms in various safe reinforcement learning environments. In order to better help the community study this problem, SafePO is developed with the following key features:
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<img src="assets/arch.png" width="800" border="1"/>
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**Correctness.** For a benchmark, it is critical to ensure its correctness and reliability.
To achieve this goal, we examine the implementation of SafePO carefully.
Firstly, each algorithm is implemented strictly according to the original paper (e.g., ensuring consistency with the gradient flow of the original paper, etc). Secondly, for algorithms with a commonly acknowledged open-source code base, we compare our implementation with those line by line, in order to double-check the correctness. Finally, we compare SafePO with existing benchmarks (e.g., [Safety-Starter-Agents](https://github.com/openai/safety-starter-agents) and [RL-Safety-Algorithms](https://github.com/SvenGronauer/RL-Safety-Algorithms)) outperforms other existing implementations.
**Extensibility.** SafePO enjoys high extensibility thanks to its architecture. New algorithms can be integrated to SafePO by inheriting from base algorithms and only implementing their unique features. For example, we integrate PPO by inheriting from policy gradient and only adding the clip ratio variable and rewriting the function that computes the loss of policy. In a similar way, algorithms can be easily added to SafePO.
**Logging and Visualization.** Another important functionality of SafePO is logging and visualization. Supporting both TensorBoard and WandB, we offer code for the visualizations of more than 40 parameters and intermediate computation results, for the purpose of inspecting the training process. Common parameters and metrics such as KL-divergence, SPS (step per second), and variance of cost are visualized universally. During training, users are able to inspect the changes of every parameter, collect the log file, and obtain saved checkpoint models. The complete and comprehensive visualization allows easier observation, model selection, and comparison.
**Documentation.** In addition to its code implementation, SafePO comes with an [extensive documentation](https://safe-policy-oExcerpt of 20,675 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:b101cf1813739942, llm:Repository topics: benchmarks, constrained-reinforcement-learning, reinforcement-learning-algorithms, safe, safe-reinforcement-learning; description: 'NeurIPS 2023: Safe Policy Optimization: A benchmark repository for safe reinforcement learning algorithms'.
matched fp:b101cf1813739942, llm:Repository topics: benchmarks, constrained-reinforcement-learning, reinforcement-learning-algorithms, safe, safe-reinforcement-learning; description: 'NeurIPS 2023: Safe Policy Optimization: A benchmark repository for safe reinforcement learning algorithms'.
matched fp:b101cf1813739942, llm:Repository topics: benchmarks, constrained-reinforcement-learning, reinforcement-learning-algorithms, safe, safe-reinforcement-learning; description: 'NeurIPS 2023: Safe Policy Optimization: A benchmark repository for safe reinforcement learning algorithms'.