I was using MDP on my work to make optimal decision. I used discrete time, finite state MDP. I assumed that I will have an initial parameters, like the Reward/Cost, state transition probabilities and actions, to make the optimal decisions. Then, I used the value iteration algorithm to find the expected values for each possible state under the given MDP configuration.
Here is my problem. As I understand, after getting the optimal policy from Value Iteration Algorithm, the policy will stay valid under the initial MDP configuration. However, I believe the policy must be adjusted whenever there is change on MDP configurations (specifically change in Reward/Cost and Transition probabilities). So, I am looking for the best solution that adjust the policy instead of recalculating the policy again using VIA under the new MDP configuration.
My work assumes the states and action doesn't change, only the cost and transition probability changes. Please suggest me any solution or reference to similar works. Thanks.