From a theoretical computer science point of view, many aspects of distance preservation of emed-dings are well understood.

We present a novel framework for unsupervised object-centric 3D scene understanding that generalizes robustly to out-of-distribution images.

Meanwhile, diverseskill-based policies are generated through a novel skill-based policy learning method. To promote efficient skill discovery, a mutual information based intrinsic reward function is constructed.

Yetinreinforcementlearning,duetothenatureofthe Bellman equation, there isanopportunity toalsoexploit temporal regularization based on smoothness in value estimates over trajectories. This paper explores a class of methods for temporal regularization.

By Jensen'logp(x) ElogR, so logR is log-likelihood.