Creating an AI player for a video game through machine learning is certainly possible, but it can be a challenging and time-consuming task, especially if you are new to machine learning. However, there are some tools and services that can make the process easier.

One approach you could take is to use a platform like OpenAI's Gym, which provides a collection of environments for training and evaluating reinforcement learning agents, including games like Atari and Go. While your game may not be part of Gym's existing collection, you may be able to create a custom environment that simulates your game and allows you to train an AI player through reinforcement learning.

To create a custom environment, you'll need to define the game rules and mechanics in a way that can be easily simulated and interacted with by the AI agent. This can be done using a programming language like Python, and you can use Gym's API to define the actions that the agent can take, the rewards it receives, and the observations it makes of the game state.

Once you have a custom environment defined, you can use existing reinforcement learning algorithms, such as Q-learning or policy gradient methods, to train your AI player to play the game. You can use libraries like TensorFlow or PyTorch to implement these algorithms, and you can use cloud computing platforms like Google Cloud or Amazon Web Services to speed up the training process by running your models on powerful hardware.

It's important to note that this approach requires a significant amount of time and effort to implement, and success is not guaranteed. However, if you're willing to put in the work, you can learn a lot about machine learning and develop a unique AI player for your game that you can challenge yourself and others with.
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