Help Needed: Adapting pre_trained_policy_action.py for Dual Policy in Multi-Policy Hierarchical RL

[Hisamichi]

Dear All,

I need help.

I’ve been working on a project involving hierarchical reinforcement learning with two low-level policies, using the navigation demo code as a reference, but I’ve hit a roadblock.

My plan is to input the two policy information into pre_trained_policy_action.py, process them based on commands, select the appropriate policy for each actor, generate a new action tensor, and apply it to the simulation using self._low_level_action_term.apply_actions().

However, I’m struggling to correctly handle the command retrieval process within this script.

What is the proper way to implement command retrieval in pre_trained_policy_action.py?
Alternatively, would it be inappropriate to modify this script to include custom logic?

Any help would be greatly appreciated!

Isaac Sim Version

4.2.0
4.1.0
4.0.0
2023.1.1
2023.1.0-hotfix.1
Other (please specify):

Isaac Lab Version (if applicable)

1.2
1.1
1.0
Other (please specify):

Operating System

Ubuntu 22.04
Ubuntu 20.04
Windows 11
Windows 10
Other (please specify):

GPU Information

• Model: RTX A4000
• Driver Version:

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