Using the Monte Carlo Tree Search Algorithm for a Card Game AI: Simulation

When the <code>Simulation</code> method is called, we use the <code>GameState</code> of the given <code>MCTS_Node</code> to create a hypothetical round of the card game. We simulate each phase in order, before finally simulating the switching of the turn. The Play Phase is the meat of the turn, where the AI is able to play cards to the gameboard. We use an infinite loop to get a random possible action from the game state, breaking the loop if that action is to end the turn. Otherwise we apply it to the game state, and get another action. This then continues to simulate the round, until eventually assigning it a Score. This score is how beneficial the theorised game state is for the AI. <a href="https://medium.com/@mattgmez98/using-the-monte-carlo-tree-search-algorithm-for-a-card-game-ai-simulation-40a0218494e4">Click Here</a>