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

Shuang Ao et al. (2022)

> Home

Details and statistics

DOI: —

access: open

type: Conference or Workshop Paper

metadata version: 2024-08-04

- view
  - electronic edition @ mlr.press (open access)
- export record
  dblp key:
  - conf/icml/AoZ0LSZ22
- ask others
- share record
  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

manage site settings

To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.