_:n3059af24cd0142988d00b550533faa2fxb26
<http://schema.org/description>
"In Markov Decision Processes (MDPs), the reward obtained in a state depends\non the properties of the last state and action. This state dependency makes it\ndifficult to reward more interesting long-term behaviors, such as always\nclosing a door after it has been opened, or providing coffee only following a\nrequest. Extending MDPs to handle such non-Markovian reward function was the\nsubject of two previous lines of work, both using variants of LTL to specify\nthe reward function and then compiling the new model back into a Markovian\nmodel. Building upon recent progress in the theories of temporal logics over\nfinite traces, we adopt LDLf for specifying non-Markovian rewards and provide\nan elegant automata construction for building a Markovian model, which extends\nthat of previous work and offers strong minimality and compositionality\nguarantees."
<https://cdnjs.deepai.org/profile/fabio-patrizi>