We introduce a new formulation of the Hidden Parameter Markov Decision
Process (HiP-MDP), a framework for modeling families of related tasks using
low-dimensional latent embeddings. Our new framework correctly models the joint
uncertainty in the latent parameters and the state space. We also replace the
original Gaussian Process-based model with a Bayesian Neural Network, enabling
more scalable inference. Thus, we expand the scope of the HiP-MDP to
applications with higher dimensions and more complex dynamics.

我们引入了一个新的 HiP-MDP 隐藏参数马尔可夫决策过程的处理方式，它能够通过低维度的潜在嵌入来建模相关任务的家族。我们采用贝叶斯神经网络来替换原模型中的高斯过程，从而使推理更具可扩展性，我们正确地模拟了潜在参数和状态空间中的联合不确定性，并扩大了 HiP-MDP 的应用范围，可以应用于维度更高且具有更复杂动态的问题。

基于隐含参数马尔可夫决策过程的强健高效迁移学习

Robust and Efficient Transfer Learning with Hidden-Parameter Markov  Decision Processes

Control applications often feature tasks with similar, but not identical,
dynamics. We introduce the Hidden Parameter Markov Decision Process (HiP-MDP),
a framework that parametrizes a family of related dynamical systems with a
low-dimensional set of latent factors, and introduce a semiparametric
regression approach for learning its structure from data. In the control
setting, we show that a learned HiP-MDP rapidly identifies the dynamics of a
new task instance, allowing an agent to flexibly adapt to task variations.

该论文介绍了一种名为隐藏参数马尔科夫决策过程 (HiP-MDP) 的框架，用于对控制应用中的不同动态系统进行参数化，并提出了一种半参数回归方法来学习其结构。在控制设置中，学习的 HiP-MDP 能够快速识别新任务实例的动态，使得智能体能够灵活适应任务的变化。