  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.
