I would like to build a model based on reinforcement learning (RL) for the following scenario
Recommend the best route (of cities listed for a given country) that satisfies the required criteria (museum, beaches, food, etc) for a total budget of $2000.
Based on the recommendation, the user will provide its feedback (as a reward), so the recommendations can be fine-tuned (by reinforcement learning) the next time. I modeled the system this way:
States = (c,cr), where $c$ is the city and $cr$ is the criteria (history, beach, food, etc)
Actions = (p) is the price of visiting the city
Reward: acceptance of the cities selected by end user as a route (1 or 0)
The objective is to decide which list of cities together satisfy the given budget.
Is this MDP model right and how can I implement this? May be the only option is using Monte Carlo methods and linear/dynamic programming.. Is there any other way?