Hi,
we’re currently looking into using LA to train FPS style bots that use raycast information, e.g. a vision grid. Typically, I would feed that into a 2D convolutional layer to extract information, but as far as a I can see, the LA plugin doesn’t let me do that (please advise if I missed something). Hence my question, which isn’t really limited to convolutional layers: which parts do I need to touch to expose additional pytorch functionality?
- nne_runtime_basic_cpu_pytorch.py seems to handle the conversion between what the engine sent/receives and the pytorch API
- LearningObservation.cpp builds the data that is send to the trainer process
- LearningAgentsObservations.cpp exposes that to the user code
Did I miss anything?
(I can of course edit the plugin code, but it would be great if the plugin had some extension points, so I could make my implementation available to people who do not fancy recompiling the engine 
)
1 Like
1d and 2d conv are available on UE-main (from github). And will be coming to UE 5.7
1 Like
How can we effectively extend the Learning Agents plugin to support more complex tasks?
Sweat, thanks! Will check it out! I had it actually half-way implemented it myself, but I can abandon that now.
General question remains though. LA plugin and expecially the BasicCPU runtime module feel very closed off and everything is hidden away behind interfaces or private utility functions. Though I can swap out the model used in the python trainer with my own script, and produce an ONNX artefact for NNE, but that defeats a bit the purpose of the LA plugin to architectect models from C++.
If I could wish for something, I’d like to have a “custom” layer, where I can manually specify what’s serialized back and forth, which python module to use for training, and provide an evaluation function for the runtime.
1 Like
What would you like to do that you can’t? LA’s lower level public API is pretty flexible and you can mix in extra stuff fairly easily, e.g. calling GatherObservations etc. instead of RunTraining.
@_YAF_Lightbringer
Something like that sounds good. We would need to work out the details but it might be feasible.
I just tried the Conv2d layer on ue-main (b1a5dcf49535bbdc388b3f1dd81bd72fe7c37d74), seems to work. Thanks.
There’s a compilation error though that needs to be fixed. In LearningTrainer.cpp:721 ff. the function UE::Learning::Trainer::IsObservationSchemaSubsetCompatible is missing a case for Conv2. It’s simple enough to add (I can open a PR if that’s easier for you):
case Observation::EType::Conv2d:
{
const Observation::FSchemaConv2dParameters Parameters = ObservationSchema.GetConv2d(ObservationSchemaElement);
if (Object->GetStringField(TEXT("Type")) != TEXT("Conv2d"))
{
return false;
}
return IsObservationSchemaSubsetCompatible(Object->GetObjectField(TEXT("Element")).ToSharedRef(), ObservationSchema, Parameters.Element);
}
There was also the fluke that my code didn’t run anymore until I set CustomTrainerModulePath in the trainer process settings to an absolute path, despite me not having moved any files around. But that may also be due to the fact that I’m configuring everything via code and not assets. So, I might have missed a new UPROPERTY initalizer of something.
1 Like
Good catch. Weirdly enough it compiles fine for us but we’ll have a fix up shortly