CLUTR: Curriculum Learning via Unsupervised Task Representation Learning. (arXiv:2210.10243v1 [cs.LG])

Reinforcement Learning (RL) algorithms are often known for sample
inefficiency and difficult generalization. Recently, Unsupervised Environment
Design (UED) emerged as a new paradigm for zero-shot generalization by
simultaneously learning a task distribution and agent policies on the sampled
tasks. This is a non-stationary process where the task distribution evolves
along with agent policies, creating an instability over time. While past works
demonstrated the potential of such approaches, sampling effectively from the
task space remains an open challenge, bottlenecking these approaches. …

arxiv curriculum curriculum learning representation representation learning unsupervised