在加强型学习系统中用伪熵进行不确定性估计

加强型学习系统是一种与没有约束的，未知的环境相互作用的系统，学习系统的目标在大最大可能地获取累积奖励信号，这个奖励信号在有限，未知的生命周期由系统所处的环境中得到，对于一个加强型学习系统，困难之一在于奖励信号非常稀疏，尤其是对于只有时延信号的系统，已有的加强型学习方法以价值函数的形式贮存奖励信号，例如著名的Ｑ－学习。本文提出了一个基于状态的不生估计模型的方法，这个算法对有利用存贮于价值函数中的奖励

Uncertainty Estimate with Pseudo-Entropy in Reinforcement Learning

A reinforcement learning (RL) system interacts with an unrestricted, unknown environment. Its goal is to maximize cumulative rewards, to be obtained throughout its limited, unknown lifetime. One of difficulties for a RL system is that reward signal is sparse, specially for RL system with very delayed rewards. In this paper, we describe an algorithm based on a model of the state's uncertainty estimate. It uses efficiently reward information stored in value function. The experiments show that the algorithm has a very good performance.