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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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.23372 |
| Publication time | Mon, 25 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-25T04:07:35.648Z |
| Last seen | 2026-05-25T04:07:35.648Z |
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| Summary source text | contentText |
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| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
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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Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
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