资源分类
》 资源 》论文》Inverse Reinforcement Learning in Partially Observable Environments

Inverse Reinforcement Learning in Partially Observable Environments

2019-11-16 | |  112 |   56 |   0

Abstract Inverse reinforcement learning (IRL) is the problem of recovering the underlying reward function from the behaviour of an expert. Most of the existing algorithms for IRL assume that the expert’s environment is modeled as a Markov decision process (MDP), although they should be able to handle partially observable settings in order to widen the applicability to more realistic scenarios. In this paper, we present an extension of the classical IRL algorithm by Ng and Russell to partially observable environments. We discuss technical issues and challenges, and present the experimental results on some of the benchmark partially observable domains

上一篇：Active Policy Iteration: Efficient Exploration through Active Learning for Value Function Approximation in Reinforcement Learning

下一篇：Optimal Incremental Preference Elicitation during Negotiation

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...