[Disscusion] Alternative code base for safe reinforcement learning research: OmniSafe

[zmsn-2077]

The safety-starter-agents codebase has been a valuable resource for early-stage research in the field of reinforcement learning. However, it has come to our attention that the author is no longer maintaining the library, resulting in some frustration due to the absence of updates for the latest algorithms and the lack of support for model-based, offline security reinforcement learning algorithms.

In response to this issue and inspired by the streamlined design philosophy of safety-starter-agents, we have developed an infrastructural framework, OmniSafe, aimed at accelerating safe reinforcement learning research. Our framework supports a range of algorithms, including On-policy, Off-policy, model-based, offline, and control-based approaches, with continuous updates for the latest algorithms.

Thanks to safety-starter-agents, a superb codebase, we are able to build upon the achievements of our predecessors in the field of scientific research, and we hope that OmniSafe can provide support for further scientific research in safe reinforcement learning for everyone.

The OmniSafe git repository: https://github.com/OmniSafeAI/omnisafe