[Question] SKRL - How to add new parameters in tensorboard logging? (managed RL)

[AndreaRossetto]

Question

Hi,
I need to add new log values during training, I have all the rewards but I need also to log other things, for example the joints torque but I'm not able to find how I can do it.

Where I can find an example code to do that?

[RandomOakForest]

Thank you for posting this. Have you tried track_data? SKRL allows tracking of any custom scalar metric by simply passing the name and value to track_data(). For example,

agent.track_data("Torques / mean_joint_torque", mean_joint_torque)

or

agent.writer.add_scalar("Torques / joint_torque", joint_torque_value, global_step=step)

[AndreaRossetto]

I've found it but I don't know where to put it. I've to create an CurrTerm() with that funcion or I have to modify something else? where I find the "agent" in a Managed env?

[Toni-SM]

Hi @AndreaRossetto

I don't know manager-based workflow in detail, but similar to the direct workflow, the is the self.extras["log"] dict is used to log additional/custom information to TensorBoard.

IsaacLab/source/isaaclab/isaaclab/envs/manager_based_rl_env.py

Line 367 in 3a0db9d

self.extras["log"] = dict()

Now, we need to ask to a manager-based expert about how to access it from the task config.
@Mayankm96 @kellyguo11 @ooctipus for viz

[AndreaRossetto]

No one can help me with this?