_:n3059af24cd0142988d00b550533faa2fxb34
<http://schema.org/description>
"MDPs extended with LTLf/LDLf non-Markovian rewards have recently attracted\ninterest as a way to specify rewards declaratively. In this paper, we discuss\nhow a reinforcement learning agent can learn policies fulfilling LTLf/LDLf\ngoals. In particular we focus on the case where we have two separate\nrepresentations of the world: one for the agent, using the (predefined,\npossibly low-level) features available to it, and one for the goal, expressed\nin terms of high-level (human-understandable) fluents. We formally define the\nproblem and show how it can be solved. Moreover, we provide experimental\nevidence that keeping the RL agent feature space separated from the goal's can\nwork in practice, showing interesting cases where the agent can indeed learn a\npolicy that fulfills the LTLf/LDLf goal using only its features (augmented with\nadditional memory)."
<https://cdnjs.deepai.org/profile/fabio-patrizi>