Curriculum reinforcement learning with measurable task representation learning
The article discusses a novel approach to curriculum reinforcement learning (CRL) that focuses on measurable task representation learning. This method aims to enhance automatic curriculum generation by transforming the task space into a latent space for better task similarity measurement. Experimental results indicate that this approach outperforms existing CRL methods in challenging navigation tasks.
- ▪Curriculum reinforcement learning allows agents to accumulate knowledge over a sequence of tasks.
- ▪The proposed method uses a variational autoencoder to create a latent task representation that measures task similarity.
- ▪The new approach effectively generates tasks that are increasingly similar to the target task.
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Computer Science > Machine Learning arXiv:2605.23372 (cs) [Submitted on 22 May 2026] Title:Curriculum reinforcement learning with measurable task representation learning Authors:Yongyan Wen, Siyuan Li, Mingjian Fu, Yiqin Yang, Xun Wang, Peng Liu View a PDF of the paper titled Curriculum reinforcement learning with measurable task representation learning, by Yongyan Wen and 5 other authors View PDF Abstract:In curriculum reinforcement learning (CRL), an agent incrementally accumulates knowledge over a sequence of tasks (i.e., a curriculum), and the learning process is aimed at using the accumulated knowledge to finally solve a challenging target task. While early CRL works focus on sequencing candidate tasks, recent research explores automatic curriculum generation.
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