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A few of us have spent quite a bit of time thinking about this. I summarised our work in a Medium article here: https://towardsdatascience.com/deep-learning-vs-puzzle-games-e996feb76162 Would love to hear what you think. Spoiler: so far, good old SAT seems to beat fancy AI algorithms!


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The state space is certainly continuous, assuming that you can somehow feed that AI exact coordinates. You may have to resort to CNNs if you do not have access to this information. For the action space, you should consider how the game actually plays. Since you use a mouse to simply show the direction, you could use (x,y) positions of the mouse as an action, ...


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Art of NPC creation I'm assuming this is a standard game, not a game theory application. These AIs tend to be far simpler in theory than actual artificially intelligent agents which are used to solve real world problems. The challenge of building games is that there are few right answers. A game NPC opponent could know practically nothing of the player and ...


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"Will a neural network adapt to that ?" No. The big functional difference between human mind and neural networks : human mind learns by itself, a NN not. If we call NN the net with its layers, weights, ... this is a static system, unable to learn anything new. The back-propagation algorithm that made intelligent the NN runs outside the NN itself, ...


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The behaviour when playing against "cheats" depends on how the agent has been trained, and how different the game becomes from the training scenarios. It will also depend on how much of the agent's behaviour is driven by training, and how much by just-in-time planning. In general, unless game playing bots are written specifically to detect or cope ...


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After my initial comment (where I suggest that it might not be enough info) I believe I actually came up with an idea. Start with the full set of pokemon. For every possible type, identify the count of pokemon that are strong against that type. For this, you'll end up with a List<(pokemonId, types, List<weakAgainst>)>. Minimize List<...


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