I have an online path guidance system that learns from a set of past experiences(trajectories) to provide a guidance for the user on how to cover the given space in the best way, adopting to whatever choice the user take. I have previously applied a second-order Markov model and acquired a successful rate of 73% on correctly predicting the path followed in the testing set.
Now, I would like to try another approach: Markov decision processes(Reinforcement Learning) to implement this system and use the first-order Markov transition matrix as the states transition probabilities for the model. However, from what I've understood the agent in such applications is a simulation and would follow the action given by the learned policy, but in my case the agent(user) might suddenly decides to jump from a state to another without following the given suggestion(policy). Is reinforcement learning unsuitable for my application?