资源分类
》 资源 》论文》Reinforcement Learning in Robust Markov Decision Processes

Reinforcement Learning in Robust Markov Decision Processes

2020-01-17 | |  83 |   40 |   0

Abstract

An important challenge in Markov decision processes is to ensure robustness with respect to unexpected or adversarial system behavior while taking advantage of well-behaving parts of the system. We consider a problem setting where some unknown parts of the state space can have arbitrary transitions while other parts are purely stochastic. We devise an algorithm that is adaptive to potentially adversarial behavior and show that it achieves similar regret bounds as the purely stochastic case.

上一篇：Policy Shaping: Integrating Human Feedback with Reinforcement Learning

下一篇：Pass-Efficient Unsupervised Feature Selection

用户评价
全部评价

热门资源

  • The Variational S...

    Unlike traditional images which do not offer in...

  • Learning to Predi...

    Much of model-based reinforcement learning invo...

  • Stratified Strate...

    In this paper we introduce Stratified Strategy ...

  • A Mathematical Mo...

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

  • Rating-Boosted La...

    The performance of a recommendation system reli...