Dynamic graph neural networks (DGNNs) are increasingly pervasive in
exploiting spatio-temporal patterns on dynamic graphs. However, existing works
fail to generalize under distribution shifts, which are common in real-world
scenarios. As the generation of dynamic graphs is heavily influenced by latent
environments, investigating their impacts on the out-of-distribution (OOD)
generalization is critical. However, it remains unexplored with the following
two major challenges: (1) How to properly model and infer the complex
environments on dynamic graphs with distribution shifts? (2) How to discover
invariant patterns given inferred spatio-temporal environments? To solve these
challenges, we propose a novel Environment-Aware dynamic Graph LEarning (EAGLE)
framework for OOD generalization by modeling complex coupled environments and
exploiting spatio-temporal invariant patterns. Specifically, we first design
the environment-aware EA-DGNN to model environments by multi-channel
environments disentangling. Then, we propose an environment instantiation
mechanism for environment diversification with inferred distributions. Finally,
we discriminate spatio-temporal invariant patterns for out-of-distribution
prediction by the invariant pattern recognition mechanism and perform
fine-grained causal interventions node-wisely with a mixture of instantiated
environment samples. Experiments on real-world and synthetic dynamic graph
datasets demonstrate the superiority of our method against state-of-the-art
baselines under distribution shifts. To the best of our knowledge, we are the
first to study OOD generalization on dynamic graphs from the environment
learning perspective.

环境感知的动态图学习（EAGLE）框架通过建模复杂的耦合环境和利用时空不变模式，解决了动态图在分布转移下的超出分布预测的问题。

环境感知的动态图学习用于非分布式泛化

Environment-Aware Dynamic Graph Learning for Out-of-Distribution  Generalization

Safety is an essential component for deploying reinforcement learning (RL)
algorithms in real-world scenarios, and is critical during the learning process
itself. A natural first approach toward safe RL is to manually specify
constraints on the policy's behavior. However, just as learning has enabled
progress in large-scale development of AI systems, learning safety
specifications may also be necessary to ensure safety in messy open-world
environments where manual safety specifications cannot scale. Akin to how
humans learn incrementally starting in child-safe environments, we propose to
learn how to be safe in one set of tasks and environments, and then use that
learned intuition to constrain future behaviors when learning new, modified
tasks. We empirically study this form of safety-constrained transfer learning
in three challenging domains: simulated navigation, quadruped locomotion, and
dexterous in-hand manipulation. In comparison to standard deep RL techniques
and prior approaches to safe RL, we find that our method enables the learning
of new tasks and in new environments with both substantially fewer safety
incidents, such as falling or dropping an object, and faster, more stable
learning. This suggests a path forward not only for safer RL systems, but also
for more effective RL systems.

为了将 RL 算法部署到实际场景中并在学习过程中确保安全性，我们提出了使用转移学习方法学习在一个任务环境中如何保持安全性，然后将所学用于约束在学习新任务时的行为，此方法在三个具有挑战性的领域中实证，相比于标准的深度 RL 技术和以前的安全 RL 方法，我们的方法不但减少了安全事故，还提高了学习的速度和稳定性。