A Relational Intervention Approach for Unsupervised Dynamics Generalization in Model-Based Reinforcement Learning. (arXiv:2206.04551v1 [cs.LG])

The generalization of model-based reinforcement learning (MBRL) methods to
environments with unseen transition dynamics is an important yet challenging
problem. Existing methods try to extract environment-specified information $Z$
from past transition segments to make the dynamics prediction model
generalizable to different dynamics. However, because environments are not
labelled, the extracted information inevitably contains redundant information
unrelated to the dynamics in transition segments and thus fails to maintain a
crucial property of $Z$: $Z$ should be similar in the same environment …

arxiv dynamics learning lg reinforcement reinforcement learning unsupervised