There are a number of ways in which AI can be used to solve markov decision processes that are difficult to solve. One approach is to use machine learning to learn the underlying structure of the problem and then use this knowledge to find an optimal solution. Another approach is to use large language models to generate new and creative solutions to the problem. Finally, neural networks can be used to approximate the solution to the problem.
Each of these methods has its own advantages and disadvantages, and the best approach for a given problem will depend on the specifics of the problem at hand. In general, however, all of these methods can be used to obtain good results.
Machine learning is a powerful tool for solving problems, but it can be computationally intensive. Learning the underlying structure of a problem can be difficult, and often requires a lot of data. However, once the structure is learned, it can be used to find an optimal solution.
Large language models can be used to generate new solutions to a problem. However, they may not be very accurate, and they can be computationally expensive.
Neural networks can be used to approximate the solution to a problem. They are often faster than other methods, but they can be less accurate.
Which of these methods is best depends on the specific problem to be solved. In general, however, all of these methods can be used to obtain good results.
References:
https://en.wikipedia.org/wiki/Machine_learning
https://en.wikipedia.org/wiki/Language_model
https://en.wikipedia.org/wiki/Neural_network