The article explores using model-free reinforcement learning to find the best strategies in games where players aim to meet certain goals. By simplifying these games into easier versions, the researchers showed that reinforcement learning algorithms can be used to figure out the best moves for each player. This approach doesn't need to know all the details of the game beforehand, making it more flexible. The researchers also found a way to make this process smoother for certain types of games. Overall, the study demonstrates how reinforcement learning can be applied to complex decision-making scenarios.