We propose an automata-theoretic approach for reinforcement learning (RL)
under complex spatio-temporal constraints with time windows. The problem is
formulated using a Markov decision process under a bounded temporal logic
constraint. Different from existing RL methods that can eventually learn
optimal policies satisfying such constraints, our proposed approach enforces a
desired probability of constraint satisfaction throughout learning. This is
achieved by translating the bounded temporal logic constraint into a total
automaton and avoiding "unsafe" actions based on the available prior
information regarding the transition probabilities, i.e., a pair of upper and
lower bounds for each transition probability. We provide theoretical guarantees
on the resulting probability of constraint satisfaction. We also provide
numerical results in a scenario where a robot explores the environment to
discover high-reward regions while fulfilling some periodic pick-up and
delivery tasks that are encoded as temporal logic constraints.

我们提出了一种基于自动机理论的强化学习方法，用于复杂时空约束下的限时学习。该方法通过将有界时态逻辑约束转化为总自动机，并基于已知的转移概率上下界避免 “不安全” 动作，从而在学习过程中强制满足约束条件的概率达到预期值。

带时间窗的概率时空约束下的强化学习

Reinforcement Learning Under Probabilistic Spatio-Temporal Constraints  with Time Windows

We present a novel high-resolution face swapping method using the inherent
prior knowledge of a pre-trained GAN model. Although previous research can
leverage generative priors to produce high-resolution results, their quality
can suffer from the entangled semantics of the latent space. We explicitly
disentangle the latent semantics by utilizing the progressive nature of the
generator, deriving structure attributes from the shallow layers and appearance
attributes from the deeper ones. Identity and pose information within the
structure attributes are further separated by introducing a landmark-driven
structure transfer latent direction. The disentangled latent code produces rich
generative features that incorporate feature blending to produce a plausible
swapping result. We further extend our method to video face swapping by
enforcing two spatio-temporal constraints on the latent space and the image
space. Extensive experiments demonstrate that the proposed method outperforms
state-of-the-art image/video face swapping methods in terms of hallucination
quality and consistency. Code can be found at:
this https URL.

本文提出一种基于 GAN 模型的高分辨率人脸交换方法，通过显式地将潜在空间的语义进行分离，并通过引入基于地标的结构转换潜在方向来分离结构属性中的身份和姿态信息，进而获得丰富的生成特征。通过加入空间 - 时间约束，将这种方法进一步扩展到视频人脸交换，并在实验证明，该方法在幻觉质量和连续性方面优于现有方法。