Specifying Non-Markovian Rewards in MDPs Using LDL on Finite Traces (Preliminary Version) (1706.08100v1)

Abstract: In Markov Decision Processes (MDPs), the reward obtained in a state depends on the properties of the last state and action. This state dependency makes it difficult to reward more interesting long-term behaviors, such as always closing a door after it has been opened, or providing coffee only following a request. Extending MDPs to handle such non-Markovian reward function was the subject of two previous lines of work, both using variants of LTL to specify the reward function and then compiling the new model back into a Markovian model. Building upon recent progress in the theories of temporal logics over finite traces, we adopt LDLf for specifying non-Markovian rewards and provide an elegant automata construction for building a Markovian model, which extends that of previous work and offers strong minimality and compositionality guarantees.