_:n3059af24cd0142988d00b550533faa2fxb44
<http://schema.org/description>
"One major limitation to the applicability of Reinforcement Learning (RL) to\nmany practical domains is the large number of samples required to learn an\noptimal policy. To address this problem and improve learning efficiency, we\nconsider a linear hierarchy of abstraction layers of the Markov Decision\nProcess (MDP) underlying the target domain. Each layer is an MDP representing a\ncoarser model of the one immediately below in the hierarchy. In this work, we\npropose a novel form of Reward Shaping where the solution obtained at the\nabstract level is used to offer rewards to the more concrete MDP, in such a way\nthat the abstract solution guides the learning in the more complex domain. In\ncontrast with other works in Hierarchical RL, our technique has few\nrequirements in the design of the abstract models and it is also tolerant to\nmodeling errors, thus making the proposed approach practical. We formally\nanalyze the relationship between the abstract models and the exploration\nheuristic induced in the lower-level domain. Moreover, we prove that the method\nguarantees optimal convergence and we demonstrate its effectiveness\nexperimentally."
<https://cdnjs.deepai.org/profile/fabio-patrizi>