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Cited by 133Reinforcement Learning in RoboticsDomain Adaptation and Few-Shot LearningArtificial Intelligence in Games

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Automatic Curriculum Learning For Deep RL: A Short Survey

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Portelas, Rémy, Colas, Cédric, Weng, Lilian, Hofmann, Katja, Oudeyer, Pierre-Yves·2020·Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence
DOI 10.24963/ijcai.2020/671
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Abstract

Automatic Curriculum Learning (ACL) has become a cornerstone of recent successes in Deep Reinforcement Learning (DRL). These methods shape the learning trajectories of agents by challenging them with tasks adapted to their capacities. In recent years, they have been used to improve sample efficiency and asymptotic performance, to organize exploration, to encourage generalization or to solve sparse reward problems, among others. To do so, ACL mechanisms can act on many aspects of learning problems. They can optimize domain randomization for Sim2Real transfer, organize task presentations in multi-task robotic settings, order sequences of opponents in multi-agent scenarios, etc. The ambition of this work is dual: 1) to present a compact and accessible introduction to the Automatic Curriculum Learning literature and 2) to draw a bigger picture of the current state of the art in ACL to encourage the cross-breeding of existing concepts and the emergence of new ideas.

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Research works printed but not linked: 45

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Metadata

Authors
Portelas, R., Colas, C., Weng, L., Hofmann, K., & Oudeyer, P.-Y.
Affiliations
  • Institut national de recherche en sciences et technologies du numérique (FR)
  • OpenAI (United States) (US)
  • Microsoft (United States) (US)
ORCID
Venue
Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence
Publisher
International Joint Conferences on Artificial Intelligence Organization
Published
2020-07
Pages
4819-4825
DOI
10.24963/ijcai.2020/671
OpenAlex
W3011429776
Cited by
127
References
14
Citations by year
2026: 10 · 2025: 19 · 2024: 20 · 2023: 20 · 2022: 23 · 2021: 24 · 2020: 11
FWCI
7.45
Type
conference paper
Language
en
Open access
Locations
  • International Joint Conference on Artificial Intelligence (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • HAL (submittedVersion, other-oa)
  • arXiv (Cornell University)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
  • Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
  • Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence in Games
Keywords
deep reinforcement learning, sample efficiency, exploration, generalization, sparse rewards, domain randomization, sim-to-real transfer, multi-task learning, multi-agent learning

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