Abstraction in Reinforcement Learning
Sertan Girgin
Broschiertes Buch

Abstraction in Reinforcement Learning

Using Option Discovery and State Similarity

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Reinforcement learning is the problem faced by anagent that must learn behavior throughtrial-and-error interactions with a dynamicenvironment. Usually, the problem to be solvedcontains subtasks that repeat at different regions ofthe state space. Without any guidancean agent has to learn the solutions of all subtaskinstances independently, which in turn degrades theperformance of the learning process. In this work, wepropose two novel approaches for building theconnections between different regions of the searchspace. The first approach efficiently discoversabstractions in the form of condition...