A common challenge for learning when applied to a complex ``target'' task is that learning that task all at once can be too difficult due to inefficient exploration given a sparse reward signal. Curriculum Learning addresses this challenge by sequencing training tasks for a learner to facilitate gradual learning. One of the crucial steps in finding a suitable curriculum learning approach is to understand the dimensions along which the domain can be factorized. In this paper, we identify different types of factorizations common in the literature of curriculum learning for reinforcement learning tasks: factorizations that involve the agent, the environment, or the mission. For each factorization category, we identify the relevant algorithms and techniques that leverage that factorization and present several case studies to showcase how leveraging an appropriate factorization can boost learning using a simple curriculum.

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In Decision Awareness in Reinforcement Learning (DARL) workshop t the 39th International Conference on Machine Learning (ICML), Baltimore, Maryland, USA, July 2022.

@inproceedings{DARL22-REUTH, title={Task Factorization in Curriculum Learning}, author={Reuth Mirsky and Shahaf S. Shperberg and Yulin Zhang and Zifan Xu and Yuqian Jiang and Jiaxun Cui and Peter Stone}, booktitle={Decision Awareness in Reinforcement Learning (DARL) workshop t the 39th International Conference on Machine Learning (ICML)}, month={July}, address={Baltimore, Maryland, USA}, url="http://www.cs.utexas.edu/users/ai-lab?DARL22-REUTH", year={2022} }