First of all, thank you very much for writing a pytorch implementation of DDPG+HER.
I found that this implementation works very well for all of the Fetcher environments available in gym.
Example: FetchPickAndPlace-v1

However, using the same approach for the Hand environments doesn't seem to work as well.
Example: HandManipulateEgg-v0

It's understandable that the performance wouldn't be as good since the Hand environments seem more difficult than the Fetcher environments. I was hoping that I could increase the success rate by increasing the number of epochs, but the problem at hand doesn't seem to be that simple.

Does anyone have any suggestions for how to improve the current repository so that it achieves a higher success rate for the Hand environments?

Hello,
I have a question of fetchreach_env.
In fetchreach_env,i don't konw the gripper_vel and gripper_state really mean,because the gripper doesn't need to open or close.Mabye those obs are useful in pickandplace_env.

And I try change obs in fetchreach_env.
Like this :obs = grip_pos.
Just the coordinates of the grip,and exclude the gripper_vel and gripper_state, then I use HER to train a model ,the success rate after 10 epochs is 1.

So, in fetchreach_env do I really need the gripper_vel or gripper_state.
Thank you.

Excuse me, I ran your code, but after 30 epochs, the success_rate still 0, is there anything in the code needs modify?
Thanks

Hi, thanks for providing this code. I failed to reproduce the result you have achieved with the updated environment from gymnasium Robotics and I observed the success rate remains around 0. I have modified the code a little bit so it matches the updated environment, and I tested the code with fetchReach-v2 which works well. I'm wondering what aspects I should investigate? or Perhaps you've encountered a similar challenge in the past and could offer some pointers. Thanks for your help!!!!!

Hello, have you made any changes to the pickplace environment itself? I used DDPG+HER in stable-baseline3 and did not train the results. The reward of the environment itself is only the distance between the target point and the object, which is very simple