Curriculum Learning for Cumulative Return Maximization
This work addresses the need for efficient exploration in critical reinforcement learning tasks, such as energy grid control, but is incremental as it builds on existing curriculum learning methods.
The paper tackles the problem of maximizing cumulative return in curriculum learning for reinforcement learning, proposing a task sequencing algorithm that outperforms several metaheuristic algorithms in experiments and is validated on a micro energy grid controller optimization task.
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.