Could Someone Help me out with Exploring Advanced Techniques in Reinforcement Learning for Robotics?

Hello there,

I am working on a project that involves applying reinforcement learning (RL) techniques to robotics; specifically in the context of autonomous navigation and manipulation tasks; I have been reading up on various methods and experimenting with some standard algorithms like DQN; PPO; and SAC; but I feel like I am hitting a plateau in terms of performance and robustness.

How effective is HRL in complex robotic tasks? Are there any recommended architectures or frameworks that integrate well with existing robotics platforms?

What are the best practices for transferring policies learned in simulation to real world robots? I have heard about domain randomization and domain adaptation; but I would love to hear about practical experiences and challenges.

Has anyone experimented with MARL for coordinating multiple robots? What algorithms and strategies have proven successful in ensuring cooperation and task efficiency?

Ensuring safety and stability during training and deployment is crucial. What techniques or modifications to standard RL algorithms have you found useful in preventing unsafe behaviors or ensuring more reliable performance?

Also, I have gone through this post: https://deeptalk.lambdalabs.com/t/how-to-use-gpus-when-training-a-model/3690sap-sac which definitely helped me out a lot.

Are there any recent papers; tutorials; or resources that you would recommend for someone looking to push the boundaries of RL in robotics?

I am excited to hear about your experiences; insights; and any resources you can share. Collaboration and discussion are key in pushing the field forward; and I am eager to learn from this community.

Thank you in advance for your help and assistance.