How I enable to get trained policy of Isaac-Repose-Cube-Allegro-v0 task? #1442
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Thanks for posting this. By expert policy, do you mean the final trained policy? You may find them in this directory: |
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Thank you for your answer. I mean, I cannot get a well-trained policy using the "manager_based" Isaac-Repose-Cube-Allegro-v0 task. I was able to acquire expert policy using "Isaac-Repose-Cube-Allegro-Direct-v0" task. Although the "manager_based" task is useful to adjust many parameters (in reward and states), I cannot get expert policy. (Maybe I should change many parameters in the Isaac-Repose-Cube-Allegro-v0 task). |
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I think the first step is for us to reproduce what you're seeing. Can you give me reproduction steps for your two policies so I can see if I get the same results? Are you using the samples found here directly: https://isaac-sim.github.io/IsaacLab/main/source/overview/environments.html Are these also the shadow hand examples you're looking for? |
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Hi, I used exactly this example without any changes. I think "Isaac-Repose-Cube-Allegro-Direct-v0" has well-defined parameters (rewards), but "Isaac-Repose-Cube-Allegro-v0" doesn't. Thanks for your help. |
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Hi there,
I have been trying to train the Isaac-Repose-Cube-Allegro-v0 task using the skrl PPO algorithm.
(num_envs 2048, <1 hour)
Even though the reinforcement learning reward graph has converged, I cannot get an expert policy.
I think I need well defined shaped rewards or other learning approaches like imitation learning.
By the way, I found a good demonstration of in-hand manipulation with shadow hand. on this page
To acquire expert policy, what i do ?
Thanks
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