General-purpose, intelligent, learning agents cycle through sequences of observations, actions, and rewards that are complex, uncertain, unknown, and non-Markovian. On the other hand, reinforcement learning is well-developed for small finite state Markov decision processes (MDPs). Up to now, extracting the right state representations out of bare observations, that is, reducing the general agent setup to the MDP framework, is an art that involves significant effort by designers. The primary goal of this work is to automate the reduction process and thereby significantly expand the scope of many existing reinforcement learning algorithms and the agents that employ them. Before we can think of mechanizing this search for suitable MDPs, we need a formal objective criterion. The main contribution of this article is to develop such a criterion. I also integrate the various parts into one learning algorithm. Extensions to more realistic dynamic Bayesian networks are developed in Part II. The role of POMDPs is also considered there.

本文研究如何自动化减少任务的状态表达，以将通用的智能学习机设定为MDP框架，从而扩大许多现有强化学习算法和使用它们的机器人的范围，并开发一种形式化的客观标准作为搜索可行MDPs的指导，并在算法中将各个部分整合到一起。

特征强化学习：第一部分：非结构化MDPs