资源分类
》 资源 》论文》Robust and Efficient Transfer Learning with Hidden Parameter Markov Decision Processes

Robust and Efficient Transfer Learning with Hidden Parameter Markov Decision Processes

2020-02-10 | |  67 |   35 |   0

Abstract 

We introduce a new formulation of the Hidden Parameter Markov Decision Process (HiP-MDP), a framework for modeling families of related tasks using lowdimensional 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.

上一篇：Variance-based Regularization with Convex Objectives

下一篇：Solving Most Systems of Random Quadratic Equations

用户评价
全部评价

热门资源

  • Learning to Predi...

    Much of model-based reinforcement learning invo...

  • Stratified Strate...

    In this paper we introduce Stratified Strategy ...

  • The Variational S...

    Unlike traditional images which do not offer in...

  • A Mathematical Mo...

    Direct democracy, where each voter casts one vo...

  • Rating-Boosted La...

    The performance of a recommendation system reli...