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3

The primary issue I see is that in the loop through time steps t in every training episode, you select actions for both players (who should have opposing goals to each other), but update a single q_table (which can only ever be correct for the "perspective" of one of your two players) on both of those actions, and updating both of them using a ...


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I guess it would always be better if you can reuse existing environments to make it work for yourself. Since most of the environment codes is anyway opensourced, you can always edit it to your liking. If you want a custom environment, you can add an environment to gym like this.


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https://spacy.io/api/lemmatizer just uses lookup tables and the only upstream task it relies on is POS tagging, so it should be relatively fast. For large amounts of text, SpaCy recommends using nlp.pipe, which can work in batches and has built in support for multiprocessing (with the n_process keyword), rather than than simply nlp. Also, make sure you ...


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Mask RCNN can be a very heavy function for a simple class classification. It is designed to handle multiple object in a single image. So I would suggest you could use much simpler models like VGGnet or Resnet which are backbones of the MaskRCNN. The biggest hurdle you might face is the dataset. If you are trying to capture even small difference between ...


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I can't go into details of the algorithm but here's some intuition about what's apparently going wrong: The Sobel transformation identifies mostly-vertical and mostly-horizontal edges. For slanted edges, it also shows a response, just a bit weaker. By using a window and taking the minimum of vertical and horizontal response, you identify points where you ...


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