I decided to start learning neural networks by creating a bot for the game. One of the intermediate steps is to create a global map from a series of inaccurate overlapping sub-maps. This task can be solved using OpenCV, but this solution will be too limited and sensitive (in the future I intend to complicate the task and work directly with the map image, instead of binary masks).

I've tried the following options:

  • predict the position of a new map area within the global map. (as a probability distribution)

  • predict the new state of the global map from the old and new minimap.

I've tried a lot of options formulation of the problem of network architecture, including the idea of conjoined networks, but nothing gave any relevant results.

Some articles about solving similar problems:

Here is an example of one of the options statement of the problem:

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  • $\begingroup$ Can you please put your main question in the title? $\endgroup$ – nbro Aug 2 at 20:46
  • $\begingroup$ @nbro, thank you for your feedback! Is it better now? $\endgroup$ – Green_Wizard Aug 2 at 22:07

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