# Curriculum Learning for Cumulative Return Maximization

arXiv: https://arxiv.org/abs/1906.06178 · HTML: https://mlsanity.com/paper/1906.06178/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jun 2019.

## Built on by (most cited first, 4 of 4)

- [A Simple Approach to Continual Learning by Transferring Skill Parameters](https://mlsanity.com/paper/2110.10255/) (2021)
- [Teacher-student curriculum learning for reinforcement learning](https://mlsanity.com/paper/2210.17368/) (2022)
- [Mitigating the Stability-Plasticity Dilemma in Adaptive Train Scheduling with Curriculum-Driven Continual DQN Expansion](https://mlsanity.com/paper/2408.09838/) (2024)
- [Anticipating the Consequences of Curriculum Decisions with Large Language Models](https://mlsanity.com/paper/2610.04604/) (2026)

## Abstract (arXiv)

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.