Curriculum learning has been successfully used in reinforcement learning to
accelerate the learning process, through knowledge transfer between tasks of
increasing complexity. Critical tasks, in which suboptimal exploratory actions
must be minimized, can benefit from curriculum learning, and its ability to
shape exploration through transfer. We propose a task sequencing algorithm
maximizing the cumulative return, that is, the return obtained by the agent
across all the learning episodes. By maximizing the cumulative return, the
agent not only aims at achieving high rewards as fast as possible, but also at
doing so while limiting suboptimal actions. We experimentally compare our task
sequencing algorithm to several popular metaheuristic algorithms for
combinatorial optimization, and show that it achieves significantly better
performance on the problem of cumulative return maximization. Furthermore, we
validate our algorithm on a critical task, optimizing a home controller for a
micro energy grid.

本论文提出一种任务排序算法，旨在通过控制学习过程中的探索行为减少非最优行为，并通过实验表明在组合优化问题中能够实现显著的累积回报最大化性能，以及在微型能源网络的家庭控制器优化问题中实现验证。