Truth from Zero?
&quot;. 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.&quot;<p>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&#x27;s a two-player strategy game with a finite number of valid plays at each state, yet I&#x27;m still not sure how to deal with four key elements:<p>1. The game is random - it has a deck of cards that are shuffled.<p>2. The game has hidden information - cards in hand are hidden.<p>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.<p>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 &quot;metgame&quot; extremely important - a &quot;deck&quot; that was good a week ago may be outclassed the following week as players react.<p>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.<p>Any ideas on how best to approach this problem?<p>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 &quot;build&quot;.