Is it possible to add observation afterwards

As the title of the post, is there any possibility to add observation later? because sometimes the model can be trained for a while, retraining a model from scratch due to the new added observations can be very costly. but simply adding observation seems to break the resume of the training. I guess it is because of the input shape is different from the initial.

 

Adding to WalkLearner's question, I'd also like to ask for suggestions on how to design observations to be more flexible in the first place. Sometimes an agent has to keep track of a variable number of items in an environment. Here are the approaches I'm aware of:
1) First person visual observations with a high enough resolution to reliably detect items at some distance. Requires a ton of compute and training time.
2) Third person visual observations, like a top-down minimap type of view. Again, this seems to require a high resolution. I've tried this with relatively low resolutions, but in that case, convolution & pooling seem to strip out critical details.
3) Lots and lots of raycasts, enough to discern objects at some distance.
4) A detection method like OverlapSphere with an upper limit of detectable objects. Reserve a large enough vector observation space for the maximum number of items. Set neutral values, if less objects are present. This can result in sorting issues though, e.g. if enemies are constantly moving and change their positions and distances relative to the agent.

None of these solutions seem ideal. Are there any tricks, workarounds or data structures one might utilize here? Thanks.