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Curriculum learning in reinforcement learning is used to shape exploration bypresenting the agent with increasingly complex tasks. The idea of curriculumlearning has been largely applied in both animal training and pedagogy. Inreinforcement learning, all previous task sequencing methods have shapedexploration with the objective of reducing the time to reach a givenperformance level. We propose novel uses of curriculum learning, which arisefrom choosing different objective functions. Furthermore, we define a generaloptimization framework for task sequencing and evaluate the performance ofpopular metaheuristic search methods on several tasks. We show that curriculumlearning can be successfully used to: improve the initial performance, takefewer suboptimal actions during exploration, and discover better policies.

An Optimization Framework for Task Sequencing in Curriculum Learning