Reinforcement Learning (RL) has achievedimpressiveprogress inrecent years, such asresults in Atari [24] and Go [28] in which RL agents even perform better than human beings.

Self-supervised heterogeneous graph learning (SHGL) has shown promising potential in diverse scenarios.

The traditional paradigm for TSG generally assumes that relevant segments always exist within a given video. However, this assumption is restrictive and unrealistic in real-world applications where the existence of a query-related segment isuncertain, easily resulting inerroneous grounding.

Knowing how the classifiers perform on unseen OOD data remains as a challenging task, owing tothetrain-test distribution divergence [3].

In this work, we introduce a framework for policy optimization based on mirror descent that naturally accommodates general parameterizations. The policy class induced by our scheme recovers known classes, e.g., softmax, and generates new ones depending on the choice of mirror map.