Suggestion: Request for RLLIB Support in MANISKILL

[fengjungui]

Suggestion: Request for RLLIB Support in MANISKILL

Dear MANISKILL Team,

Hello!

First, I would like to express my gratitude for the incredible work you have done in advancing robotic reinforcement learning and skill libraries. MANISKILL has provided powerful tools that greatly contribute to the development of robotics technologies. I would like to make a suggestion, hoping that future versions of MANISKILL will include support for the RLLIB library, which would further enhance its flexibility and applicability in reinforcement learning tasks.

Background:

Currently, many reinforcement learning researchers and practitioners rely on RLLIB (https://github.com/ray-project/ray/tree/master/rllib) as a standard library to implement various RL algorithms. Built on the Ray distributed framework, RLLIB offers excellent scalability and flexibility, especially in large-scale training and multi-task parallel processing. This library has already demonstrated remarkable performance in multiple areas, such as multi-agent learning and distributed training.

Reasons for Request:

Compatibility and Extensibility: RLLIB is already widely used and compatible with many reinforcement learning frameworks. Integrating RLLIB support into MANISKILL would allow users to seamlessly leverage RLLIB’s wide range of RL algorithms and tools. This is particularly beneficial for multi-task training and fine-tuning, significantly improving efficiency.

Simplified User Workflow: Many developers are already familiar with RLLIB’s API and functionalities. By adding RLLIB support, users can continue using their existing RLLIB code in the MANISKILL environment, reducing the learning curve and accelerating the development process.

Improved Research Efficiency: RLLIB provides a variety of advanced features, such as policy gradient methods, PPO, DQN, A3C, and more. Native support for these algorithms would greatly enhance performance in robotic skill training, especially for tasks involving large-scale training and high-dimensional action spaces.

Multi-platform Support: The distributed capabilities of RLLIB and its efficient hardware resource management (including CPU, GPU, etc.) would complement MANISKILL's platform, helping to better utilize hardware resources and speed up training.

[StoneT2000]

Happy to support RLLIB although I myself may not have time myself to look deeply into this for now. Happy to accept a pull request / provide suggestions on how to do it though.