". It announces an algorithm called AlphaZero that, given the rules of any two-player game of strategy and copious hardware, trains a deep neural network to play the game at skill levels approaching perfection."
I have a particular interest in having a computer play the game Magic: the Gathering at an expert level. In some ways, this seems like an ideal fit for AlphaZero - it's a two-player strategy game with a finite number of valid plays at each state, yet I'm still not sure how to deal with four key elements:
1. The game is random - it has a deck of cards that are shuffled.
2. The game has hidden information - cards in hand are hidden.
3. The cards can change the rules of the game itself. For example, a card could change the win conditions of the game. There are over 20,000 unique cards with more regularly being printed.
4. A major part of the game (perhaps the most important) is that each player chooses their own combination of sixty cards from the ever-increasing pool. This makes understanding the "metgame" extremely important - a "deck" that was good a week ago may be outclassed the following week as players react.
In addition, there are many decks that win with odd combinations of cards and often the only way to beat these decks is to know in advance how to stop the combo.
Any ideas on how best to approach this problem?
Right now I am working on building a very simple version of the game with both players playing the same three cards with no randomness. This will eventually feed a simple genetic algorithm to test different quantities of each of the three cards to find an optimal "build".
I have a particular interest in having a computer play the game Magic: the Gathering at an expert level. In some ways, this seems like an ideal fit for AlphaZero - it's a two-player strategy game with a finite number of valid plays at each state, yet I'm still not sure how to deal with four key elements:
1. The game is random - it has a deck of cards that are shuffled.
2. The game has hidden information - cards in hand are hidden.
3. The cards can change the rules of the game itself. For example, a card could change the win conditions of the game. There are over 20,000 unique cards with more regularly being printed.
4. A major part of the game (perhaps the most important) is that each player chooses their own combination of sixty cards from the ever-increasing pool. This makes understanding the "metgame" extremely important - a "deck" that was good a week ago may be outclassed the following week as players react.
In addition, there are many decks that win with odd combinations of cards and often the only way to beat these decks is to know in advance how to stop the combo.
Any ideas on how best to approach this problem?
Right now I am working on building a very simple version of the game with both players playing the same three cards with no randomness. This will eventually feed a simple genetic algorithm to test different quantities of each of the three cards to find an optimal "build".