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I want to implement an AI capable of playing the game RoboRally (https://en.wikipedia.org/wiki/RoboRally) using Monte Carlo Tree Search (MCTS). In RoboRally, there are 2-8 characters controlled by (human) players, each of which must reach checkpoints. However, each turn, a character can only move by playing cards chosen by the player associated with that character. For example, the card MOVE2 allows a character to move 2 tiles in the direction they are facing on the gameboard.

In each turn, the player selects 5 cards from their hand of 9 cards, which are then played in the chosen order. Each node in the MCTS represents a game state with a certain set of chosen cards. For example, a node expanded from the root might represent playing the MOVE2 card first for a player. My question is: how should I simulate a game state to a terminal state in this case? As I understand it, a terminal state would be reached when 5 cards are chosen, i.e., the fifth level of the MCTS is reached.

Considering the example where only MOVE2 is chosen first, should the simulation involve randomly choosing the remaining 4 cards from my hand of 9 cards and then simulate the game state with these chosen cards? This evalution might then be how far the character is from the next checkpoint. The closer the character is to the next checkpoint, the better the evaluation, i.e., value of the node. Furthermore, is MCTS even a suitable choice for this game, considering that reaching a terminal state does not evaluate to a win/loss/draw situation, and I must evaluate the simulations with a function, which might be very complicated.

If MCTS is not suitable, what other algorithms would be appropriate for implementing an AI for this kind of game?

I would appreciate any advice and feedback!

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