Learning in non-stationary Partially Observable Markov Decision Processes
We study the problem of finding an optimal policy for a Partially Observable Markov Decision Process (POMDP) when the model is not perfectly known and may change over time. We present the algorithm MEDUSA+, which incrementally improves a POMDP model using selected queries, while still optimizing the reward. Empirical results show the response of the algorithm to changes in the parameters of a model: the changes are learned quickly and the agent still accumulates high reward throughout the process.