通常利用 POMDPs 对在部分可观测的随机环境中决策的 agents 建模。针对完整 POMDP 的求解方法扩展能力弱的问题，提出把多元 POMDP 分解成多个受限制的 POMDPs，然后独立求解每个模型，以获得值函数，并将这些受限制的 POMDPs 的值函数结合起来以便获得完整 POMDP 策略。该方法主要阐述识别与独立任务相关的状态变量的过程，以及如何构造被限制在单独任务上的模型。将该方法应用到两个不同规模的岩石采样问题中，实验结果表明，该方法能够获得很好的策略。%POMDPs are widely used to model agents acting in a stochastic environment under partial observability.Because the complete POMDP solvers have poor ability to scale up,this paper proposed to decompose a factored POMDP into a set of restricted POMDPs and solved each such model independently,acquiring a value function.And then,it used the combination of the value functions of the restricted POMDPs to form a policy for the complete POMDP.It mainly explained the process of identifying state variables that corresponded to independent tasks,and how to create a model restricted to a single task.Using this method on RockSample domain with two different size,experiment results show that this method can gain a good policy.
展开▼
