2022-03-21 14:27:16 +01:00
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# Project vacuumDecay
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Project vacuumDecay is a framework for building AIs for games.
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Avaible architectures are
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- those used in Deep Blue (mini-max / expecti-max)
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- advanced expecti-max exploration based on utility heuristics
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- those used in AlphaGo Zero (knowledge distilation using neural-networks)
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A new AI is created by subclassing the State-class and defining the following functionality (mycelia.py provies a template):
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- initialization (generating the gameboard or similar)
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- getting avaible actions for the current situation (returns an Action-object, which can be subclassed to add additional functionality)
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- applying an action (the state itself should be immutable, a new state should be returned)
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- checking for a winning-condition (should return None if game has not yet ended)
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- (optional) a getter for a string-representation of the current state
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- (optional) a heuristic for the winning-condition (greatly improves capability)
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- (optional) a getter for a tensor that describes the current game state (required for knowledge distilation)
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- (optional) interface to allow a human to select an action
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### Current state of the project
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2022-04-15 14:35:58 +02:00
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The only thing that currently works is the AI for Ultimate TicTacToe.
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It uses a trained neural heuristic (neuristic)
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2022-05-13 17:06:35 +02:00
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You can train it or play against it (will also train it) using 'python ultimatetictactoe.py'
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The performance of the trained neuristic is pretty bad. I have some ideas on what could be the problems but no time to implement fixes.
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(Focus on the ending of games at the beginning of training; more consistent exploration-depth for expanding while training; ...)
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