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"EAT-C: Environment-Adversarial sub-Task Curriculum for Efficient ..."

Shuang Ao et al. (2022)

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DOI: —

access: open

type: Conference or Workshop Paper

metadata version: 2024-08-04

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  - electronic edition @ mlr.press (open access)
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  dblp key:
  - conf/icml/AoZ0LSZ22
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  persistent URL:
  - https://dblp.org/rec/conf/icml/AoZ0LSZ22
Shuang Ao, Tianyi Zhou, Jing Jiang, Guodong Long, Xuan Song, Chengqi Zhang:
EAT-C: Environment-Adversarial sub-Task Curriculum for Efficient Reinforcement Learning. ICML 2022: 822-843

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