Goal Oriented Action Planning - Optimizations & Extensions скачать в хорошем качестве
Goal Oriented Action Planning - Optimizations & Extensions
2 года назад
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AlphaGO Zero
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Goal Oriented Action Planning - Optimizations & Extensions
GOAP - letting characters plan actions oriented at goals. In the last part of the GOAP tutorial series we will talk about AlphaGO Zero to give some inspiration for more complex GOAP applications and optimizations of the exponential runtime complexity and the space complexity. We consider A* for planning as a solution but the requirements like a heuristic, iterative deepening A* and the infinite search space make it unpractical and overkill to use A* as planning algorithm. I propose a load balancing / scheduling approach for GOAP. In the second part we look at Google's AlphaGO Zero and its planning capabilities which serve as inspiration for extension of our GOAP system. We look at action choosing to simplify search trees, considering the opponent's moves and learned discontentment functions or rating functions as opposed to handcrafted discontentment functions. These discontentment functions can be learned by making them linear weighted functions and then machine learning the weights. Get the implementations: C# (Unity) implementation: https://github.com/Elias-W1/GOAP-C-Sharp Python implementation: https://github.com/Elias-W1/GOAP-Python Have fun! :) Music by Millennials Melody
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