Symbolic Dynamic Programming within the Fluent Calculus
Symbolic Dynamic Programming within the Fluent Calculus
复制标题
Fluent 微积分中的符号动态规划
DOI：
--
复制
发表时间：
2002
期刊：
影响因子：
--
通讯作者：
Steffen Hölldobler
中科院分区：
文献类型：
--
作者：
Axel Grossmann;Steffen Hölldobler
给出了一种基于流演算的一阶马尔可夫决策过程的符号动态规划建模方法。基于最初在[3]中提出的思想，马尔可夫决策过程的主要组成部分，如最优值函数和策略逻辑表示。该技术产生一组一阶公式的平等，最小限度地划分状态空间。因此，这里提出的符号动态规划算法不需要枚举状态和动作空间，从而解决了经典动态规划方法的缺点。此外，我们说明了如何有条件的行动和特异性可以建模的方法。
A symbolic dynamic programming approach for modelling first-order Markov decision processes within the fluent calculus is given. Based on an idea initially presented in [3], the major components of Markov decision processes such as the optimal value function and a policy are logically represented. The technique produces a set of first-order formulae with equality that minimally partitions the state space. Consequently, the symbolic dynamic programming algorithm presented here does not require to enumerate the state and action spaces, thereby solving a drawback of classical dynamic programming methods. In addition, we illustrate how conditional actions and specificity can be modelled by the approach.