Custom model for multisensor environments

[Benatti1991]

Hello everyone,
I would like to create an algorithm to train multi sensor agent using your DRL framework.
What I have in mind is concatenating one or more convolutional layers whose input could be cameras or lidar sensor with 1D arrays from other sensors (such as GPS).
It looks like I should add an option to inputs.py and a custom model to manage this kind of environments. Would this be enough? Do you have any suggestion?
Thanks,
Simone

[araffin]

Related to #133

[Miffyli]

arrafin beat me to it again <.<.

Just for more direct link: Here is an example on how to combine visual observation with 1D vector: #133 (comment)

[Benatti1991]

Araffin, Miffly,
thank you for your answers. Actually I knew about the issue you linked, but I opened a new one for two reasons

• Creating a new layer in the image input to fill it only with some data would mean wasting a lot of memory (the appended layer would be mostly padded zeros)
• This approach wouldn't be feasible if there are more convolutional inputs with different resolutions.
  So, back to my question: what should I change (besides, obviously, a custom model) to manage multi-sensor environments?
  Thanks,
  Simone

[Miffyli]

As discussed in #133, true multi-modal observations are not currently possible and you have to resort to this kind of dirty hacks for now. However this is the very next thing on the to-do list after TF2 support, which is slowly getting there but currently on a hiatus due to holidays :)

[araffin]

Closing in favor of #133