# Curriculum Learning for Cumulative Return Maximization

## TL;DR

This paper introduces a curriculum learning algorithm that sequences tasks to maximize cumulative return in reinforcement learning, improving learning speed and reducing suboptimal actions, validated on energy grid control.

## Contribution

The paper proposes a novel task sequencing algorithm specifically designed to maximize cumulative return in reinforcement learning, enhancing exploration and efficiency.

## Key findings

- The algorithm outperforms popular metaheuristics in cumulative return maximization.
- It effectively reduces suboptimal exploratory actions during learning.
- Validated on a micro energy grid control task, showing practical applicability.

## Abstract

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.