【文件属性】:
文件名称:Bayesian Reinforcement Learning A Survey
文件大小:1.81MB
文件格式:PDF
更新时间:2022-01-28 19:24:09
贝叶斯 增强学习 机器学习 深度学习 机器人
Bayesian methods for machine learning have been widely investigated,
yielding principled methods for incorporating prior information into
inference algorithms. In this survey, we provide an in-depth review
of the role of Bayesian methods for the reinforcement learning (RL)
paradigm. The major incentives for incorporating Bayesian reasoning
in RL are: 1) it provides an elegant approach to action-selection (exploration/
exploitation) as a function of the uncertainty in learning; and
2) it provides a machinery to incorporate prior knowledge into the algorithms.
We first discuss models and methods for Bayesian inference
in the simple single-step Bandit model. We then review the extensive
recent literature on Bayesian methods for model-based RL, where prior
information can be expressed on the parameters of the Markov model.
We also present Bayesian methods for model-free RL, where priors are
expressed over the value function or policy class. The objective of the
paper is to provide a comprehensive survey on Bayesian RL algorithms
and their theoretical and empirical properties.