On the Benefit of Optimal Transport for Curriculum Reinforcement Learning

TL;DR

This paper introduces a novel approach to curriculum reinforcement learning by framing curriculum generation as a constrained optimal transport problem, leading to improved performance across diverse tasks.

Contribution

It proposes a new method that formulates curriculum generation as an optimal transport problem, enhancing existing CRL techniques.

Findings

Outperforms existing CRL methods on benchmark tasks.

Effectively generates curricula as interpolations between task distributions.

Demonstrates versatility across different task characteristics.

Abstract

Curriculum reinforcement learning (CRL) allows solving complex tasks by generating a tailored sequence of learning tasks, starting from easy ones and subsequently increasing their difficulty. Although the potential of curricula in RL has been clearly shown in various works, it is less clear how to generate them for a given learning environment, resulting in various methods aiming to automate this task. In this work, we focus on framing curricula as interpolations between task distributions, which has previously been shown to be a viable approach to CRL. Identifying key issues of existing methods, we frame the generation of a curriculum as a constrained optimal transport problem between task distributions. Benchmarks show that this way of curriculum generation can improve upon existing CRL methods, yielding high performance in various tasks with different characteristics.